quimb.tensor ¶

amplitude(b, optimize='auto-hq', simplify_sequence='ADCRS', simplify_atol=1e-12, simplify_equalize_norms=True, backend=None, dtype=None, rehearse=False)[source]¶

Get the amplitude coefficient of bitstring b.

\[c_b = \langle b | \psi \rangle\]

Parameters:

b (str or sequence of int) – The bitstring to compute the transition amplitude for.
optimize (str, optional) – Contraction path optimizer to use for the amplitude, can be a non-reusable path optimizer as only called once (though path won’t be cached for later use in that case).
simplify_sequence (str, optional) – Which local tensor network simplifications to perform and in which order, see full_simplify().
simplify_atol (float, optional) – The tolerance with which to compare to zero when applying full_simplify().
simplify_equalize_norms (bool, optional) – Actively renormalize tensor norms during simplification.
backend (str, optional) – Backend to perform the contraction with, e.g. 'numpy', 'cupy' or 'jax'. Passed to cotengra.
dtype (str, optional) – Data type to cast the TN to before contraction.
rehearse (bool or "tn", optional) – If True, generate and cache the simplified tensor network and contraction tree but don’t actually perform the contraction. Returns a dict with keys "tn" and 'tree' with the tensor network that will be contracted and the corresponding contraction tree if so.

amplitude_rehearse(b='random', simplify_sequence='ADCRS', simplify_atol=1e-12, simplify_equalize_norms=True, optimize='auto-hq', dtype=None, rehearse=True)[source]¶

Perform just the tensor network simplifications and contraction tree finding associated with computing a single amplitude (caching the results) but don’t perform the actual contraction.

Parameters:

b ('random', str or sequence of int) – The bitstring to rehearse computing the transition amplitude for, if 'random' (the default) a random bitstring will be used.
optimize (str, optional) – Contraction path optimizer to use for the marginal, can be a non-reusable path optimizer as only called once (though path won’t be cached for later use in that case).
simplify_sequence (str, optional) – Which local tensor network simplifications to perform and in which order, see full_simplify().
simplify_atol (float, optional) – The tolerance with which to compare to zero when applying full_simplify().
simplify_equalize_norms (bool, optional) – Actively renormalize tensor norms during simplification.
backend (str, optional) – Backend to perform the marginal contraction with, e.g. 'numpy', 'cupy' or 'jax'. Passed to cotengra.
dtype (str, optional) – Data type to cast the TN to before contraction.

Return type:

dict

amplitude_tn[source]¶

partial_trace(keep, optimize='auto-hq', simplify_sequence='ADCRS', simplify_atol=1e-12, simplify_equalize_norms=True, backend=None, dtype=None, rehearse=False)[source]¶

Perform the partial trace on the circuit wavefunction, retaining only qubits in keep, and making use of reverse lightcone cancellation:

\[\rho_{\bar{q}} = Tr_{\bar{p}} |\psi_{\bar{q}} \rangle \langle \psi_{\bar{q}}|\]

Where \(\bar{q}\) is the set of qubits to keep, \(\psi_{\bar{q}}\) is the circuit wavefunction only with gates in the causal cone of this set, and \(\bar{p}\) is the remaining qubits.

Parameters:

keep (int or sequence of int) – The qubit(s) to keep as we trace out the rest.
optimize (str, optional) – Contraction path optimizer to use for the reduced density matrix, can be a non-reusable path optimizer as only called once (though path won’t be cached for later use in that case).
simplify_sequence (str, optional) – Which local tensor network simplifications to perform and in which order, see full_simplify().
simplify_atol (float, optional) – The tolerance with which to compare to zero when applying full_simplify().
simplify_equalize_norms (bool, optional) – Actively renormalize tensor norms during simplification.
backend (str, optional) – Backend to perform the marginal contraction with, e.g. 'numpy', 'cupy' or 'jax'. Passed to cotengra.
dtype (str, optional) – Data type to cast the TN to before contraction.
rehearse (bool or "tn", optional) – If True, generate and cache the simplified tensor network and contraction tree but don’t actually perform the contraction. Returns a dict with keys "tn" and 'tree' with the tensor network that will be contracted and the corresponding contraction tree if so.

Return type:

array or dict

partial_trace_rehearse[source]¶

partial_trace_tn[source]¶

local_expectation(G, where, optimize='auto-hq', simplify_sequence='ADCRS', simplify_atol=1e-12, simplify_equalize_norms=True, backend=None, dtype=None, rehearse=False)[source]¶

Compute the a single expectation value of operator G, acting on sites where, making use of reverse lightcone cancellation.

\[\langle \psi_{\bar{q}} | G_{\bar{q}} | \psi_{\bar{q}} \rangle\]

where \(\bar{q}\) is the set of qubits \(G\) acts one and \(\psi_{\bar{q}}\) is the circuit wavefunction only with gates in the causal cone of this set. If you supply a tuple or list of gates then the expectations will be computed simultaneously.

Parameters:

G (array or sequence[array]) – The raw operator(s) to find the expectation of.
where (int or sequence of int) – Which qubits the operator acts on.
optimize (str, optional) – Contraction path optimizer to use for the local expectation, can be a non-reusable path optimizer as only called once (though path won’t be cached for later use in that case).
simplify_sequence (str, optional) – Which local tensor network simplifications to perform and in which order, see full_simplify().
simplify_atol (float, optional) – The tolerance with which to compare to zero when applying full_simplify().
simplify_equalize_norms (bool, optional) – Actively renormalize tensor norms during simplification.
backend (str, optional) – Backend to perform the marginal contraction with, e.g. 'numpy', 'cupy' or 'jax'. Passed to cotengra.
dtype (str, optional) – Data type to cast the TN to before contraction.
gate_opts (None or dict_like) – Options to use when applying G to the wavefunction.
rehearse (bool or "tn", optional) – If True, generate and cache the simplified tensor network and contraction tree but don’t actually perform the contraction. Returns a dict with keys 'tn' and 'tree' with the tensor network that will be contracted and the corresponding contraction tree if so.

Return type:

scalar, tuple[scalar] or dict

local_expectation_rehearse[source]¶

local_expectation_tn[source]¶

compute_marginal(where, fix=None, optimize='auto-hq', backend=None, dtype='complex64', simplify_sequence='ADCRS', simplify_atol=1e-06, simplify_equalize_norms=True, rehearse=False)[source]¶

Compute the probability tensor of qubits in where, given possibly fixed qubits in fix and tracing everything else having removed redundant unitary gates.

Parameters:

where (sequence of int) – The qubits to compute the marginal probability distribution of.
fix (None or dict[int, str], optional) – Measurement results on other qubits to fix.
optimize (str, optional) – Contraction path optimizer to use for the marginal, can be a non-reusable path optimizer as only called once (though path won’t be cached for later use in that case).
backend (str, optional) – Backend to perform the marginal contraction with, e.g. 'numpy', 'cupy' or 'jax'. Passed to cotengra.
dtype (str, optional) – Data type to cast the TN to before contraction.
simplify_sequence (str, optional) – Which local tensor network simplifications to perform and in which order, see full_simplify().
simplify_atol (float, optional) – The tolerance with which to compare to zero when applying full_simplify().
simplify_equalize_norms (bool, optional) – Actively renormalize tensor norms during simplification.
rehearse (bool or "tn", optional) – Whether to perform the marginal contraction or just return the associated TN and contraction tree.

compute_marginal_rehearse[source]¶

compute_marginal_tn[source]¶

calc_qubit_ordering(qubits=None, method='greedy-lightcone')[source]¶

Get a order to measure qubits in, by greedily choosing whichever has the smallest reverse lightcone followed by whichever expands this lightcone least.

Parameters:: qubits (None or sequence of int) – The qubits to generate a lightcone ordering for, if None, assume all qubits.
Returns:: The order to ‘measure’ qubits in.
Return type:: tuple[int]

_parse_qubits_order(qubits=None, order=None)[source]¶: Simply initializes the default of measuring all qubits, and the default order, or checks that order is a permutation of qubits.

_group_order(order, group_size=1)[source]¶: Take the qubit ordering order and batch it in groups of size group_size, sorting the qubits (for caching reasons) within each group.

get_qubit_distances(method='dijkstra', alpha=2)[source]¶

Get a nested dictionary of qubit distances. This is computed from a graph representing qubit interactions. The graph has an edge between qubits if they are acted on by the same gate, and the distance-weight of the edge is exponentially small in the number of gates between them.

Parameters:

method ({'dijkstra', 'resistance'}, optional) – The method to use to compute the qubit distances. See networkx.all_pairs_dijkstra_path_length() and networkx.resistance_distance().
alpha (float, optional) – The distance weight between qubits is alpha**(num_gates - 1 ).

Returns:

The distance between each pair of qubits, accessed like distances[q1][q2]. If two qubits are not connected, the distance is missing.

Return type:

dict[int, dict[int, float]]

reordered_gates_dfs_clustered()[source]¶: Get the gates reordered by a depth first search traversal of the multi-qubit gate graph that greedily selects successive gates which are ‘close’ in graph distance, and shifts single qubit gates to be adjacent to multi-qubit gates where possible.

sample(C, qubits=None, order=None, group_size=10, max_marginal_storage=2**20, seed=None, optimize='auto-hq', backend=None, dtype='complex64', simplify_sequence='ADCRS', simplify_atol=1e-06, simplify_equalize_norms=True)[source]¶

Sample the circuit given by gates, C times, using lightcone cancelling and caching marginal distribution results. This is a generator. This proceeds as a chain of marginal computations.

Assuming we have group_size=1, and some ordering of the qubits, \(\{q_0, q_1, q_2, q_3, \ldots\}\) we first compute:

\[p(q_0) = \mathrm{diag} \mathrm{Tr}_{1, 2, 3,\ldots} | \psi_{0} \rangle \langle \psi_{0} |\]

I.e. simply the probability distribution on a single qubit, conditioned on nothing. The subscript on \(\psi\) refers to the fact that we only need gates from the causal cone of qubit 0. From this we can sample an outcome, either 0 or 1, if we call this \(r_0\) we can then move on to the next marginal:

\[p(q_1 | r_0) = \mathrm{diag} \mathrm{Tr}_{2, 3,\ldots} \langle r_0 | \psi_{0, 1} \rangle \langle \psi_{0, 1} | r_0 \rangle\]

I.e. the probability distribution of the next qubit, given our prior result. We can sample from this to get \(r_1\). Then we compute:

\[p(q_2 | r_0 r_1) = \mathrm{diag} \mathrm{Tr}_{3,\ldots} \langle r_0 r_1 | \psi_{0, 1, 2} \rangle \langle \psi_{0, 1, 2} | r_0 r_1 \rangle\]

Eventually we will reach the ‘final marginal’, which we can compute as

\[|\langle r_0 r_1 r_2 r_3 \ldots | \psi \rangle|^2\]

since there is nothing left to trace out.

Parameters:

C (int) – The number of times to sample.
qubits (None or sequence of int, optional) – Which qubits to measure, defaults (None) to all qubits.
order (None or sequence of int, optional) – Which order to measure the qubits in, defaults (None) to an order based on greedily expanding the smallest reverse lightcone. If specified it should be a permutation of qubits.
group_size (int, optional) – How many qubits to group together into marginals, the larger this is the fewer marginals need to be computed, which can be faster at the cost of higher memory. The marginal themselves will each be of size 2**group_size.
max_marginal_storage (int, optional) – The total cumulative number of marginal probabilites to cache, once this is exceeded caching will be turned off.
seed (None or int, optional) – A random seed, passed to numpy.random.seed if given.
optimize (str, optional) – Contraction path optimizer to use for the marginals, shouldn’t be a non-reusable path optimizer as called on many different TNs. Passed to cotengra.array_contract_tree().
backend (str, optional) – Backend to perform the marginal contraction with, e.g. 'numpy', 'cupy' or 'jax'. Passed to cotengra.
dtype (str, optional) – Data type to cast the TN to before contraction.
simplify_sequence (str, optional) – Which local tensor network simplifications to perform and in which order, see full_simplify().
simplify_atol (float, optional) – The tolerance with which to compare to zero when applying full_simplify().
simplify_equalize_norms (bool, optional) – Actively renormalize tensor norms during simplification.

Yields:

bitstrings (sequence of str)

sample_rehearse(qubits=None, order=None, group_size=10, result=None, optimize='auto-hq', simplify_sequence='ADCRS', simplify_atol=1e-06, simplify_equalize_norms=True, rehearse=True, progbar=False)[source]¶

Perform the preparations and contraction tree findings for sample(), caching various intermedidate objects, but don’t perform the main contractions.

Parameters:

qubits (None or sequence of int, optional) – Which qubits to measure, defaults (None) to all qubits.
order (None or sequence of int, optional) – Which order to measure the qubits in, defaults (None) to an order based on greedily expanding the smallest reverse lightcone.
group_size (int, optional) – How many qubits to group together into marginals, the larger this is the fewer marginals need to be computed, which can be faster at the cost of higher memory. The marginal’s size itself is exponential in group_size.
result (None or dict[int, str], optional) – Explicitly check the computational cost of this result, assumed to be all zeros if not given.
optimize (str, optional) – Contraction path optimizer to use for the marginals, shouldn’t be a non-reusable path optimizer as called on many different TNs. Passed to cotengra.array_contract_tree().
simplify_sequence (str, optional) – Which local tensor network simplifications to perform and in which order, see full_simplify().
simplify_atol (float, optional) – The tolerance with which to compare to zero when applying full_simplify().
simplify_equalize_norms (bool, optional) – Actively renormalize tensor norms during simplification.
progbar (bool, optional) – Whether to show the progress of finding each contraction tree.

Returns:

One contraction tree object per grouped marginal computation. The keys of the dict are the qubits the marginal is computed for, the values are a dict containing a representative simplified tensor network (key: ‘tn’) and the main contraction tree (key: ‘tree’).

Return type:

dict[tuple[int], dict]

sample_tns[source]¶

sample_chaotic(C, marginal_qubits, fix=None, max_marginal_storage=2**20, seed=None, optimize='auto-hq', backend=None, dtype='complex64', simplify_sequence='ADCRS', simplify_atol=1e-06, simplify_equalize_norms=True)[source]¶

Sample from this circuit, assuming it to be chaotic. Which is to say, only compute and sample correctly from the final marginal, assuming that the distribution on the other qubits is uniform. Given marginal_qubits=5 for instance, for each sample a random bit-string \(r_0 r_1 r_2 \ldots r_{N - 6}\) for the remaining \(N - 5\) qubits will be chosen, then the final marginal will be computed as

\[p(q_{N-5}q_{N-4}q_{N-3}q_{N-2}q_{N-1} | r_0 r_1 r_2 \ldots r_{N-6}) = |\langle r_0 r_1 r_2 \ldots r_{N - 6} | \psi \rangle|^2\]

and then sampled from. Note the expression on the right hand side has 5 open indices here and so is a tensor, however if marginal_qubits is not too big then the cost of contracting this is very similar to a single amplitude.

Note

This method assumes the circuit is chaotic, if its not, then the samples produced will not be an accurate representation of the probability distribution.

Parameters:

C (int) – The number of times to sample.
marginal_qubits (int or sequence of int) – The number of qubits to treat as marginal, or the actual qubits. If an int is given then the qubits treated as marginal will be circuit.calc_qubit_ordering()[:marginal_qubits].
fix (None or dict[int, str], optional) – Measurement results on other qubits to fix. These will be randomly sampled if fix is not given or a qubit is missing.
seed (None or int, optional) – A random seed, passed to numpy.random.seed if given.
optimize (str, optional) – Contraction path optimizer to use for the marginal, can be a non-reusable path optimizer as only called once (though path won’t be cached for later use in that case).
backend (str, optional) – Backend to perform the marginal contraction with, e.g. 'numpy', 'cupy' or 'jax'. Passed to cotengra.
dtype (str, optional) – Data type to cast the TN to before contraction.
simplify_sequence (str, optional) – Which local tensor network simplifications to perform and in which order, see full_simplify().
simplify_atol (float, optional) – The tolerance with which to compare to zero when applying full_simplify().
simplify_equalize_norms (bool, optional) – Actively renormalize tensor norms during simplification.

Yields:

str

sample_chaotic_rehearse(marginal_qubits, result=None, optimize='auto-hq', simplify_sequence='ADCRS', simplify_atol=1e-06, simplify_equalize_norms=True, dtype='complex64', rehearse=True)[source]¶

Rehearse chaotic sampling (perform just the TN simplifications and contraction tree finding).

Parameters:

marginal_qubits (int or sequence of int) – The number of qubits to treat as marginal, or the actual qubits. If an int is given then the qubits treated as marginal will be circuit.calc_qubit_ordering()[:marginal_qubits].
result (None or dict[int, str], optional) – Explicitly check the computational cost of this result, assumed to be all zeros if not given.
optimize (str, optional) – Contraction path optimizer to use for the marginal, can be a non-reusable path optimizer as only called once (though path won’t be cached for later use in that case).
simplify_sequence (str, optional) – Which local tensor network simplifications to perform and in which order, see full_simplify().
simplify_atol (float, optional) – The tolerance with which to compare to zero when applying full_simplify().
simplify_equalize_norms (bool, optional) – Actively renormalize tensor norms during simplification.
dtype (str, optional) – Data type to cast the TN to before contraction.

Returns:

The contraction path information for the main computation, the key is the qubits that formed the final marginal. The value is itself a dict with keys 'tn' - a representative tensor network - and 'tree' - the contraction tree.

Return type:

dict[tuple[int], dict]

sample_chaotic_tn[source]¶

get_gate_by_gate_circuits(group_size=10)[source]¶

Get a sequence of circuits by partitioning the gates into groups such circuit i + 1 acts on at most group_size new qubits compared to circuit i.

Parameters:: group_size (int, optional) – The maximum number of new qubits that can be acted on by a circuit compared to its predecessor.
Returns:: A sequence of dicts, each with keys 'circuit' and 'where', where the former is a Circuit and the latter the tuple of new qubits that it acts on comparaed to the previous circuit.
Return type:: Sequence[dict]

sample_gate_by_gate(C, group_size=10, seed=None, max_marginal_storage=2**20, optimize='auto-hq', backend=None, dtype='complex64', simplify_sequence='ADCRS', simplify_atol=1e-06, simplify_equalize_norms=True)[source]¶

Sample this circuit using the gate-by-gate method, where we ‘evolve’ a result bitstring by sequentially including more and more gates, at each step updating the result by computing a full conditional marginal. See “How to simulate quantum measurement without computing marginals” by Sergey Bravyi, David Gosset, Yinchen Liu (https://arxiv.org/abs/2112.08499). The overall complexity of this is guaranteed to be similar to that of computing a single amplitude which can be much better than the naive “qubit-by-qubit” (.sample) method. However, it requires evaluting a number of tensor networks that scales linearly with the number of gates which can offset any practical advantages for shallow circuits for example.

Parameters:

C (int) – The number of samples to generate.
group_size (int, optional) – The maximum number of qubits that can be acted on by a circuit compared to its predecessor. This will be the dimension of the marginal computed at each step.
seed (None or int, optional) – A random seed, passed to numpy.random.seed if given.
max_marginal_storage (int, optional) – The total cumulative number of marginal probabilites to cache, once this is exceeded caching will be turned off.
optimize (str, optional) – Contraction path optimizer to use for the marginals, shouldn’t be a non-reusable path optimizer as called on many different TNs. Passed to cotengra.array_contract_tree().
backend (str, optional) – Backend to perform the marginal contraction with, e.g. 'numpy', 'cupy' or 'jax'. Passed to cotengra.
dtype (str, optional) – Data type to cast the TN to before contraction.
simplify_sequence (str, optional) – Which local tensor network simplifications to perform and in which order, see full_simplify().
simplify_atol (float, optional) – The tolerance with which to compare to zero when applying full_simplify().
simplify_equalize_norms (bool, optional) – Actively renormalize tensor norms during simplification.
rehearse (bool, optional) – If True, generate and cache the simplified tensor network and contraction tree but don’t actually perform the contraction. Returns a dict with keys 'tn' and 'tree' with the tensor network that will be contracted and the corresponding contraction tree if so.

Yields:

str

sample_gate_by_gate_rehearse(group_size=10, optimize='auto-hq', dtype='complex64', simplify_sequence='ADCRS', simplify_atol=1e-06, simplify_equalize_norms=True, rehearse=True, progbar=False)[source]¶

Perform the preparations and contraction tree findings for sample_gate_by_gate(), caching various intermedidate objects, but don’t perform the main contractions.

Parameters:

group_size (int, optional) – The maximum number of qubits that can be acted on by a circuit compared to its predecessor. This will be the dimension of the marginal computed at each step.
optimize (str, optional) – Contraction path optimizer to use for the marginals, shouldn’t be a non-reusable path optimizer as called on many different TNs. Passed to cotengra.array_contract_tree().
dtype (str, optional) – Data type to cast the TN to before contraction.
simplify_sequence (str, optional) – Which local tensor network simplifications to perform and in which order, see full_simplify().
simplify_atol (float, optional) – The tolerance with which to compare to zero when applying full_simplify().
simplify_equalize_norms (bool, optional) – Actively renormalize tensor norms during simplification.
rehearse (True or "tn", optional) – If True, generate and cache the simplified tensor network and contraction tree but don’t actually perform the contraction. If “tn”, only generate the simplified tensor networks.

Return type:

Sequence[dict] or Sequence[TensorNetwork]

sample_gate_by_gate_tns[source]¶

to_dense(reverse=False, optimize='auto-hq', simplify_sequence='R', simplify_atol=1e-12, simplify_equalize_norms=True, backend=None, dtype=None, rehearse=False)[source]¶

Generate the dense representation of the final wavefunction.

Parameters:

reverse (bool, optional) – Whether to reverse the order of the subsystems, to match the convention of qiskit for example.
optimize (str, optional) – Contraction path optimizer to use for the contraction, can be a non-reusable path optimizer as only called once (though path won’t be cached for later use in that case).
dtype (str, optional) – If given, convert the tensors to this dtype prior to contraction.
simplify_sequence (str, optional) – Which local tensor network simplifications to perform and in which order, see full_simplify().
simplify_atol (float, optional) – The tolerance with which to compare to zero when applying full_simplify().
simplify_equalize_norms (bool, optional) – Actively renormalize tensor norms during simplification.
backend (str, optional) – Backend to perform the contraction with, e.g. 'numpy', 'cupy' or 'jax'. Passed to cotengra.
dtype – Data type to cast the TN to before contraction.
rehearse (bool, optional) – If True, generate and cache the simplified tensor network and contraction tree but don’t actually perform the contraction. Returns a dict with keys 'tn' and 'tree' with the tensor network that will be contracted and the corresponding contraction tree if so.

Returns:

psi – The densely represented wavefunction with dtype data.

Return type:

qarray

to_dense_rehearse[source]¶

to_dense_tn[source]¶

simulate_counts(C, seed=None, reverse=False, **to_dense_opts)[source]¶

Simulate measuring all qubits in the computational basis many times. Unlike sample(), this generates all the samples simultaneously using the full wavefunction constructed from to_dense(), then calling simulate_counts().

Warning

Because this constructs the full wavefunction it always requires exponential memory in the number of qubits, regardless of circuit depth and structure.

Parameters:

C (int) – The number of ‘experimental runs’, i.e. total counts.
seed (int, optional) – A seed for reproducibility.
reverse (bool, optional) – Whether to reverse the order of the subsystems, to match the convention of qiskit for example.
to_dense_opts – Suppled to to_dense().

Returns:

results – The number of recorded counts for each

Return type:

dict[str, int]

schrodinger_contract(*args, **contract_opts)[source]¶

xeb(samples_or_counts, cache=None, cache_maxsize=2**20, progbar=False, **amplitude_opts)[source]¶

Compute the linear cross entropy benchmark (XEB) for samples or counts, amplitude per amplitude.

Parameters:

samples_or_counts (Iterable[str] or Dict[str, int]) – Either the raw bitstring samples or a dict mapping bitstrings to the number of counts observed.
cache (dict, optional) – A dictionary to store the probabilities in, if not supplied quimb.utils.LRU(cache_maxsize) will be used.
cache_maxsize – The maximum size of the cache to be used.
optional – The maximum size of the cache to be used.
progbar – Whether to show progress as the bitstrings are iterated over.
optional – Whether to show progress as the bitstrings are iterated over.
amplitude_opts – Supplied to amplitude().

xeb_ex(optimize='auto-hq', simplify_sequence='R', simplify_atol=1e-12, simplify_equalize_norms=True, dtype=None, backend=None, autojit=False, progbar=False, **contract_opts)[source]¶

Compute the exactly expected XEB for this circuit. The main feature here is that if you supply a cotengra optimizer that searches for sliced indices then the XEB will be computed without constructing the full wavefunction.

Parameters:

optimize (str or PathOptimizer, optional) – Contraction path optimizer.
simplify_sequence (str, optional) – Simplifications to apply to tensor network prior to contraction.
simplify_sequence – Which local tensor network simplifications to perform and in which order, see full_simplify().
simplify_atol (float, optional) – The tolerance with which to compare to zero when applying full_simplify().
dtype (str, optional) – Data type to cast the TN to before contraction.
backend (str, optional) – Convert tensors to, and then use contractions from, this library.
autojit (bool, optional) – Apply autoray.autojit to the contraciton and map-reduce.
progbar (bool, optional) – Show progress in terms of number of wavefunction chunks processed.

update_params_from(tn)[source]¶

Assuming tn is a tensor network with tensors tagged GATE_{i} corresponding to this circuit (e.g. from circ.psi or circ.uni) but with updated parameters, update the current circuit parameters and tensors with those values.

This is an inplace modification of the Circuit.

Parameters:: tn (TensorNetwork) – The tensor network to find the updated parameters from.

draw(figsize=None, radius=1 / 3, drawcolor=(0.5, 0.5, 0.5), linewidth=1)[source]¶

Draw a simple linear schematic of the circuit.

Parameters:

figsize (tuple, optional) – The size of the figure, if not given will be set based on the number of gates and qubits.
radius (float, optional) – The radius of the gates.
drawcolor (tuple, optional) – The color of the wires.
linewidth (float, optional) – The linewidth of the wires.

Returns:

fig (matplotlib.Figure) – The figure object.
ax (matplotlib.Axes) – The axis object.

__repr__()[source]¶

class quimb.tensor.CircuitDense(N=None, psi0=None, gate_opts=None, gate_contract=True, tags=None, convert_eager=True, **circuit_opts)[source]¶

Bases: Circuit

Quantum circuit simulation keeping the state in full dense form.

property psi¶: Tensor network representation of the wavefunction.

property uni¶

calc_qubit_ordering(qubits=None)[source]¶: Qubit ordering doesn’t matter for a dense wavefunction.

get_psi_reverse_lightcone(where, keep_psi0=False)[source]¶: Override get_psi_reverse_lightcone as for a dense wavefunction the lightcone is not meaningful.

class quimb.tensor.CircuitMPS(N=None, *, psi0=None, max_bond=None, cutoff=1e-10, gate_opts=None, gate_contract='auto-mps', dtype=None, to_backend=None, convert_eager=True, **circuit_opts)[source]¶

Bases: Circuit

Quantum circuit simulation keeping the state always in a MPS form. If you think the circuit will not build up much entanglement, or you just want to keep a rigorous handle on how much entanglement is present, this can be useful.

Parameters:

N (int, optional) – The number of qubits in the circuit.
psi0 (TensorNetwork1DVector, optional) – The initial state, assumed to be |00000....0> if not given. The state is always copied and the tag PSI0 added.
max_bond (int, optional) – The maximum bond dimension to truncate to when applying gates, if any. This is simply a shortcut for setting gate_opts['max_bond'].
cutoff (float, optional) – The singular value cutoff to use when truncating the state. This is simply a shortcut for setting gate_opts['cutoff'].
gate_opts (dict, optional) – Default options to pass to each gate, for example, “max_bond” and “cutoff” etc.
gate_contract (str, optional) –
The default method for applying gates. Relevant MPS options are:
- 'auto-mps': automatically choose a method that maintains the MPS form (default). This uses 'swap+split' for 2-qubit gates and 'nonlocal' for 3+ qubit gates.
- 'swap+split': swap nonlocal qubits to be next to each other, before applying the gate, then swapping them back
- 'nonlocal': turn the gate into a potentially nonlocal (sub) MPO and apply it directly. See tensor_network_1d_compress().
dtype (str, optional) – The data type to use for the state tensor.
to_backend (callable, optional) – A function to convert tensor data to a particular backend.
convert_eager (bool, optional) – Whether to eagerly perform dtype casting and application of to_backend as gates are supplied, or wait until after the necessary TNs for a particular task such as sampling are formed and simplified. Eager conversion (convert_eager=True) is the default mode for MPS simulation, unlike full contraction.
circuit_opts – Supplied to Circuit.

psi¶

The current state of the circuit, always in MPS form.

Type:: MatrixProductState

Examples

Create a circuit object that always uses the “nonlocal” method for contracting in gates, and the “dm” compression method within that, using a large cutoff and maximum bond dimension:

circ = qtn.CircuitMPS(
    N=56,
    gate_opts=dict(
        contract="nonlocal",
        method="dm",
        max_bond=1024,
        cutoff=1e-3,
    )
)

_init_state(N, dtype='complex128')[source]¶

apply_gates(gates, progbar=False, **gate_opts)[source]¶

Apply a sequence of gates to this tensor network quantum circuit.

Parameters:

gates (Sequence[Gate] or Sequence[Tuple]) – The sequence of gates to apply.
gate_opts – Supplied to apply_gate().

property psi¶: Tensor network representation of the wavefunction.

property uni¶

calc_qubit_ordering(qubits=None)[source]¶: MPS already has a natural ordering.

get_psi_reverse_lightcone(where, keep_psi0=False)[source]¶: Override get_psi_reverse_lightcone as for an MPS the lightcone is not meaningful.

sample(C, seed=None, dtype=None, *, qubits=None, order=None, group_size=None, max_marginal_storage=None, optimize=None, backend=None, simplify_sequence=None, simplify_atol=None, simplify_equalize_norms=None)[source]¶

Sample the MPS circuit C times.

Parameters:

C (int) – The number of samples to generate.
seed (None, int, or generator, optional) – A random seed or generator to use for reproducibility.

fidelity_estimate()[source]¶: Estimate the fidelity of the current state based on its norm, which tracks how much the state has been truncated:

\[\tilde{F} = \left| \langle \psi | \psi \rangle \right|^2 \approx \left|\langle \psi_\mathrm{ideal} | \psi \rangle\right|^2\]

See also

error_estimate

error_estimate()[source]¶: Estimate the error in the current state based on the norm of the discarded part of the state:

\[\epsilon = 1 - \tilde{F}\]

See also

fidelity_estimate

local_expectation(G, where, normalized=False, dtype=None, *, simplify_sequence=None, simplify_atol=None, simplify_equalize_norms=None, backend=None, rehearse=None, **contract_opts)[source]¶

Compute the local expectation value of a local operator at where (via forming the reduced density matrix). Note this moves the orthogonality around inplace, and records it in info.

Parameters:

G (Tensor) – The local operator tensor.
where (int) – The qubit to compute the expectation value at.
normalized (bool, optional) – Whether to normalize the expectation value by the norm of the state.
dtype (dtype, optional) – If given, ensure the TN is cast to this dtype before contracting.

Return type:

class quimb.tensor.CircuitPermMPS(N=None, psi0=None, gate_opts=None, gate_contract='swap+split', **circuit_opts)[source]¶

Bases: CircuitMPS

Quantum circuit simulation keeping the state always in an MPS form, but lazily tracking the qubit ordering rather than ‘swapping back’ qubits after applying non-local gates. This can be useful for circuits with no expectation of locality. The qubit ordering is always tracked in the attribute qubits. The psi attribute returns the TN with the sites reindexed and retagged according to the current qubit ordering, meaning it is no longer an MPS. Use circ.get_psi_unordered() to get the unpermuted MPS and use circ.qubits to get the current qubit ordering if you prefer.

qubits¶

_apply_gate(gate, tags=None, **gate_opts)[source]¶

Apply a Gate to this Circuit. This is the main method that all calls to apply a gate should go through.

Parameters:

gate (Gate) – The gate to apply.
tags (str or sequence of str, optional) – Tags to add to the gate tensor(s).

calc_qubit_ordering(qubits=None)[source]¶: Given by the current qubit permutation.

get_psi_unordered()[source]¶: Return the MPS representing the state but without reordering the sites.

sample(C, seed=None, dtype=None)[source]¶

Sample the PermMPS circuit C times.

Parameters:

C (int) – The number of samples to generate.
seed (None, int, or generator, optional) – A random seed or generator to use for reproducibility.

Yields:

str – The next sample bitstring.

property psi¶: Tensor network representation of the wavefunction.

class quimb.tensor.Gate(label, params, qubits=None, controls=None, round=None, parametrize=False)[source]¶

A simple class for storing the details of a quantum circuit gate.

Parameters:

label (str) – The name or ‘identifier’ of the gate.
params (Iterable[float]) – The parameters of the gate.
qubits (Iterable[int], optional) – Which qubits the gate acts on.
controls (Iterable[int], optional) – Which qubits are the controls.
round (int, optional) – If given, which round or layer the gate is part of.
parametrize (bool, optional) – Whether the gate will correspond to a parametrized tensor.

__slots__ = ('_label', '_params', '_qubits', '_controls', '_round', '_parametrize', '_tag', '_special',...¶

_label¶

_params¶

_round = 0¶

_parametrize = False¶

_tag¶

_special¶

_constant¶

_array = None¶

classmethod from_raw(U, qubits=None, controls=None, round=None)[source]¶

copy()[source]¶

property label¶

property params¶

property qubits¶

property total_qubit_count¶

property controls¶

property round¶

property special¶

property parametrize¶

property tag¶

copy_with(**kwargs)[source]¶: Take a copy of this gate but with some attributes changed.

build_array()[source]¶: Build the array representation of the gate. For controlled gates this excludes the control qubits.

property array¶

build_mpo(L=None, **kwargs)[source]¶: Build an MPO representation of this gate.

__repr__()[source]¶

quimb.tensor.circ_a2a_rand(n, depth, seed=None, gate2='cz', **circuit_opts)[source]¶

Generate a random all-to-all quantum circuit.

Parameters:

n (int) – The number of qubits.
depth (int) – The circuit depth. Each layer consists of n // 2 entangling gates applied between a random permutation of qubit pairs.
seed (int, optional) – Random seed.
gate2 ({'cx', 'cy', 'cz', 'iswap', ..., str}, optional) – The gate to use for the entanling pairs.

Return type:

quimb.tensor.circ_ansatz_1D_brickwork(n, depth, cyclic=False, gate2='cz', seed=None, **circuit_opts)[source]¶

A 1D circuit ansatz with odd and even layers of entangling gates interleaved with U3 single qubit unitaries:

|  |  |  |  |
|  u  u  u  u
u  o++o  o++o
|  u  u  u  |
o++o  o++o  u
|  u  u  u  |
u  o++o  o++o
|  u  u  u  |
o++o  o++o  u
|  u  u  u  u
u  o++o  o++o
|  u  u  u  |
o++o  o++o  u
u  u  u  u  |
|  |  |  |  |

Parameters:

n (int) – The number of qubits.
depth (int) – The number of entangling gates per pair.
cyclic (bool, optional) – Whether to add entangling gates between qubits 0 and n - 1.
gate2 ({'cx', 'cy', 'cz', 'iswap', ..., str}, optional) – The gate to use for the entanling pairs.
seed (int, optional) – Random seed for parameters.
opts – Supplied to gates_to_param_circuit().

Return type:

quimb.tensor.circ_ansatz_1D_rand(n, depth, seed=None, cyclic=False, gate2='cz', avoid_doubling=True, **circuit_opts)[source]¶

A 1D circuit ansatz with randomly place entangling gates interleaved with U3 single qubit unitaries.

Parameters:

n (int) – The number of qubits.
depth (int) – The number of entangling gates per pair.
seed (int, optional) – Random seed.
cyclic (bool, optional) – Whether to add entangling gates between qubits 0 and n - 1.
gate2 ({'cx', 'cy', 'cz', 'iswap', ..., str}, optional) – The gate to use for the entanling pairs.
avoid_doubling (bool, optional) – Whether to avoid placing an entangling gate directly above the same entangling gate (there will still be single qubit gates interleaved).
opts – Supplied to gates_to_param_circuit().

Return type:

quimb.tensor.circ_ansatz_1D_zigzag(n, depth, gate2='cz', seed=None, **circuit_opts)[source]¶

A 1D circuit ansatz with forward and backward layers of entangling gates interleaved with U3 single qubit unitaries:

|  |  |  |
u  u  |  |
o++o  u  |
|  |  |  u
|  o++o  |
|  |  u  |
|  |  o++o
u  u  u  u
|  |  o++o
|  |  u  |
|  o++o  |
|  u  |  u
o++o  u  |
u  u  |  |
|  |  |  |

Parameters:

n (int) – The number of qubits.
depth (int) – The number of entangling gates per pair.
gate2 ({'cx', 'cy', 'cz', 'iswap', ..., str}, optional) – The gate to use for the entanling pairs.
seed (int, optional) – Random seed for parameters.
opts – Supplied to gates_to_param_circuit().

Return type:

quimb.tensor.circ_qaoa(terms, depth, gammas, betas, **circuit_opts)[source]¶

Generate the QAOA circuit for weighted graph described by terms.

\[|{\bar{\gamma}, \bar{\beta}}\rangle = U_B (\beta _p) U_C (\gamma _p) \cdots U_B (\beta _1) U_C (\gamma _1) |{+}\rangle\]

with

\[U_C (\gamma) = e^{-i \gamma \mathcal{C}} = \prod \limits_{i, j \in E(G)} e^{-i \gamma w_{i j} Z_i Z_j}\]

and

\[U_B (\beta) = \prod \limits_{i \in G} e^{-i \beta X_i}\]

Parameters:

terms (dict[tuple[int], float]) – The mapping of integer pair keys (i, j) to the edge weight values, wij. The integers should be a contiguous range enumerated from zero, with the total number of qubits being inferred from this.
depth (int) – The number of layers of gates to apply, p above.
gammas (iterable of float) – The interaction angles for each layer.
betas (iterable of float) – The rotation angles for each layer.
circuit_opts – Supplied to Circuit. Note gate_opts={'contract': False} is set by default (it can be overridden) since the RZZ gate, even though it has a rank-2 decomposition, is also diagonal.

quimb.tensor.array_contract(arrays, inputs, output=None, optimize=None, backend=None, **kwargs)[source]¶

quimb.tensor.contract_backend(backend, set_globally=False)[source]¶

A context manager to temporarily set the default backend used for tensor contractions, via ‘cotengra’. By default, this only sets the contract backend for the current thread.

Parameters:: set_globally (bool, optimize) – Whether to set the backend just for this thread, or for all threads. If you are entering the context, then using multithreading, you might want True.

quimb.tensor.contract_strategy(strategy, set_globally=False)[source]¶

A context manager to temporarily set the default contraction strategy supplied as optimize to cotengra. By default, this only sets the contract strategy for the current thread.

Parameters:: set_globally (bool, optimize) – Whether to set the strategy just for this thread, or for all threads. If you are entering the context, then using multithreading, you might want True.

quimb.tensor.get_contract_backend()[source]¶: Get the default backend used for tensor contractions, via ‘cotengra’.

See also

set_contract_backend, get_tensor_linop_backend, set_tensor_linop_backend, tensor_contract

quimb.tensor.get_contract_strategy()[source]¶: Get the default contraction strategy - the option supplied as optimize to cotengra.

quimb.tensor.get_symbol[source]¶

quimb.tensor.get_tensor_linop_backend()[source]¶: Get the default backend used for tensor network linear operators, via ‘cotengra’. This is different from the default contraction backend as the contractions are likely repeatedly called many times.

See also

set_tensor_linop_backend, set_contract_backend, get_contract_backend, TNLinearOperator

quimb.tensor.inds_to_eq(inputs, output=None)[source]¶

Turn input and output indices of any sort into a single ‘equation’ string where each index is a single ‘symbol’ (unicode character).

Parameters:

inputs (sequence of sequence of hashable) – The input indices per tensor.
output (sequence of hashable) – The output indices.

Returns:

eq – The string to feed to einsum/contract.

Return type:

str

quimb.tensor.set_contract_backend(backend)[source]¶: Set the default backend used for tensor contractions, via ‘cotengra’.

See also

get_contract_backend, set_tensor_linop_backend, get_tensor_linop_backend, tensor_contract

quimb.tensor.set_contract_strategy(strategy)[source]¶: Get the default contraction strategy - the option supplied as optimize to cotengra.

quimb.tensor.set_tensor_linop_backend(backend)[source]¶: Set the default backend used for tensor network linear operators, via ‘cotengra’. This is different from the default contraction backend as the contractions are likely repeatedly called many times.

See also

get_tensor_linop_backend, set_contract_backend, get_contract_backend, TNLinearOperator

quimb.tensor.tensor_linop_backend(backend, set_globally=False)[source]¶

A context manager to temporarily set the default backend used for tensor network linear operators, via ‘cotengra’. By default, this only sets the contract backend for the current thread.

Parameters:: set_globally (bool, optimize) – Whether to set the backend just for this thread, or for all threads. If you are entering the context, then using multithreading, you might want True.

quimb.tensor.edges_1d_chain(L, cyclic=False)[source]¶

Return the graph edges of a finite 1D chain lattice.

Parameters:

L (int) – The number of cells.
cyclic (bool, optional) – Whether to use periodic boundary conditions.

Returns:

edges

Return type:

list[(int, int)]

quimb.tensor.edges_2d_hexagonal(Lx, Ly, cyclic=False, cells=None)[source]¶

Return the graph edges of a finite 2D hexagonal lattice. There are two sites per cell, and note the cells do not form a square tiling. The nodes (sites) are labelled like (i, j, s) for s in 'AB'.

Parameters:

Lx (int) – The number of cells along the x-direction.
Ly (int) – The number of cells along the y-direction.
cyclic (bool, optional) – Whether to use periodic boundary conditions.
cells (list, optional) – A list of cells to use. If not given the cells used are itertools.product(range(Lx), range(Ly)).

Returns:

edges

Return type:

list[((int, int, str), (int, int, str))]

quimb.tensor.edges_2d_kagome(Lx, Ly, cyclic=False, cells=None)[source]¶

Return the graph edges of a finite 2D kagome lattice. There are three sites per cell, and note the cells do not form a square tiling. The nodes (sites) are labelled like (i, j, s) for s in 'ABC'.

Parameters:

Lx (int) – The number of cells along the x-direction.
Ly (int) – The number of cells along the y-direction.
cyclic (bool, optional) – Whether to use periodic boundary conditions.
cells (list, optional) – A list of cells to use. If not given the cells used are itertools.product(range(Lx), range(Ly)).

Returns:

edges

Return type:

list[((int, int, str), (int, int, str))]

quimb.tensor.edges_2d_square(Lx, Ly, cyclic=False, cells=None)[source]¶

Return the graph edges of a finite 2D square lattice. The nodes (sites) are labelled like (i, j).

Parameters:

Lx (int) – The number of cells along the x-direction.
Ly (int) – The number of cells along the y-direction.
cyclic (bool, optional) – Whether to use periodic boundary conditions.
cells (list, optional) – A list of cells to use. If not given the cells used are itertools.product(range(Lx), range(Ly)).

Returns:

edges

Return type:

list[((int, int), (int, int))]

quimb.tensor.edges_2d_triangular(Lx, Ly, cyclic=False, cells=None)[source]¶

Return the graph edges of a finite 2D triangular lattice. There is a single site per cell, and note the cells do not form a square tiling. The nodes (sites) are labelled like (i, j).

Parameters:

Lx (int) – The number of cells along the x-direction.
Ly (int) – The number of cells along the y-direction.
cyclic (bool, optional) – Whether to use periodic boundary conditions.
cells (list, optional) – A list of cells to use. If not given the cells used are itertools.product(range(Lx), range(Ly)).

Returns:

edges

Return type:

list[((int, int), (int, int))]

quimb.tensor.edges_2d_triangular_rectangular(Lx, Ly, cyclic=False, cells=None)[source]¶

Return the graph edges of a finite 2D triangular lattice tiled in a rectangular geometry. There are two sites per rectangular cell. The nodes (sites) are labelled like (i, j, s) for s in 'AB'.

Parameters:

Lx (int) – The number of cells along the x-direction.
Ly (int) – The number of cells along the y-direction.
cyclic (bool, optional) – Whether to use periodic boundary conditions.
cells (list, optional) – A list of cells to use. If not given the cells used are itertools.product(range(Lx), range(Ly)).

Returns:

edges

Return type:

list[((int, int, s), (int, int, s))]

quimb.tensor.edges_3d_cubic(Lx, Ly, Lz, cyclic=False, cells=None)[source]¶

Return the graph edges of a finite 3D cubic lattice. The nodes (sites) are labelled like (i, j, k).

Parameters:

Lx (int) – The number of cells along the x-direction.
Ly (int) – The number of cells along the y-direction.
Lz (int) – The number of cells along the z-direction.
cyclic (bool, optional) – Whether to use periodic boundary conditions.
cells (list, optional) – A list of cells to use. If not given the cells used are itertools.product(range(Lx), range(Ly), range(Lz)).

Returns:

edges

Return type:

list[((int, int, int), (int, int, int))]

quimb.tensor.edges_3d_diamond(Lx, Ly, Lz, cyclic=False, cells=None)[source]¶

Return the graph edges of a finite 3D diamond lattice. There are two sites per cell, and note the cells do not form a cubic tiling. The nodes (sites) are labelled like (i, j, k, s) for s in 'AB'.

Parameters:

Lx (int) – The number of cells along the x-direction.
Ly (int) – The number of cells along the y-direction.
Lz (int) – The number of cells along the z-direction.
cyclic (bool, optional) – Whether to use periodic boundary conditions.
cells (list, optional) – A list of cells to use. If not given the cells used are itertools.product(range(Lx), range(Ly), range(Lz)).

Returns:

edges

Return type:

list[((int, int, int, str), (int, int, int, str))]

quimb.tensor.edges_3d_diamond_cubic(Lx, Ly, Lz, cyclic=False, cells=None)[source]¶

Return the graph edges of a finite 3D diamond lattice tiled in a cubic geometry. There are eight sites per cubic cell. The nodes (sites) are labelled like (i, j, k, s) for s in 'ABCDEFGH'.

Parameters:

Lx (int) – The number of cells along the x-direction.
Ly (int) – The number of cells along the y-direction.
Lz (int) – The number of cells along the z-direction.
cyclic (bool, optional) – Whether to use periodic boundary conditions.
cells (list, optional) – A list of cells to use. If not given the cells used are itertools.product(range(Lx), range(Ly), range(Lz)).

Returns:

edges

Return type:

list[((int, int, int, str), (int, int, int, str))]

quimb.tensor.edges_3d_pyrochlore(Lx, Ly, Lz, cyclic=False, cells=None)[source]¶

Return the graph edges of a finite 3D pyorchlore lattice. There are four sites per cell, and note the cells do not form a cubic tiling. The nodes (sites) are labelled like (i, j, k, s) for s in 'ABCD'.

Parameters:

Lx (int) – The number of cells along the x-direction.
Ly (int) – The number of cells along the y-direction.
Lz (int) – The number of cells along the z-direction.
cyclic (bool, optional) – Whether to use periodic boundary conditions.
cells (list, optional) – A list of cells to use. If not given the cells used are itertools.product(range(Lx), range(Ly), range(Lz)).

Returns:

edges

Return type:

list[((int, int, int, str), (int, int, int, str))]

quimb.tensor.edges_tree_rand(n, max_degree=None, seed=None)[source]¶

Return a random tree with n nodes. This a convenience function for testing purposes and the trees generated are not guaranteed to be uniformly random (for that see networkx.random_labeled_tree).

Parameters:

n (int) – The number of nodes.
max_degree (int, optional) – The maximum degree of the nodes. For example max_degree=3 means generate a binary tree.
seed (int, optional) – The random seed.

Returns:

edges

Return type:

list[(int, int)]

quimb.tensor.jax_register_pytree()[source]¶

quimb.tensor.pack(obj)[source]¶

Take a tensor or tensor network like object and return a skeleton needed to reconstruct it, and a pytree of raw parameters.

Parameters:

obj (Tensor, TensorNetwork, or similar) – Something that has copy, set_params, and get_params methods.

Returns:

params (pytree) – A pytree of raw parameter arrays.
skeleton (Tensor, TensorNetwork, or similar) – A copy of obj with all references to the original data removed.

quimb.tensor.unpack(params, skeleton)[source]¶

Take a skeleton of a tensor or tensor network like object and a pytree of raw parameters and return a new reconstructed object with those parameters inserted.

Parameters:

params (pytree) – A pytree of raw parameter arrays, with the same structure as the output of skeleton.get_params().
skeleton (Tensor, TensorNetwork, or similar) – Something that has copy, set_params, and get_params methods.

Returns:

obj – A copy of skeleton with parameters inserted.

Return type:

Tensor, TensorNetwork, or similar

class quimb.tensor.TNOptimizer(tn, loss_fn, norm_fn=None, loss_constants=None, loss_kwargs=None, tags=None, shared_tags=None, constant_tags=None, loss_target=None, optimizer='L-BFGS-B', progbar=True, bounds=None, autodiff_backend='AUTO', executor=None, callback=None, **backend_opts)[source]¶

Globally optimize tensors within a tensor network with respect to any loss function via automatic differentiation. If parametrized tensors are used, optimize the parameters rather than the raw arrays.

Parameters:

tn (TensorNetwork) – The core tensor network structure within which to optimize tensors.
loss_fn (callable or sequence of callable) – The function that takes tn (as well as loss_constants and loss_kwargs) and returns a single real ‘loss’ to be minimized. For Hamiltonians which can be represented as a sum over terms, an iterable collection of terms (e.g. list) can be given instead. In that case each term is evaluated independently and the sum taken as loss_fn. This can reduce the total memory requirements or allow for parallelization (see executor).
norm_fn (callable, optional) – A function to call before loss_fn that prepares or ‘normalizes’ the raw tensor network in some way.
loss_constants (dict, optional) – Extra tensor networks, tensors, dicts/list/tuples of arrays, or arrays which will be supplied to loss_fn but also converted to the correct backend array type.
loss_kwargs (dict, optional) – Extra options to supply to loss_fn (unlike loss_constants these are assumed to be simple options that don’t need conversion).
tags (str, or sequence of str, optional) – If supplied, only optimize tensors with any of these tags.
shared_tags (str, or sequence of str, optional) – If supplied, each tag in shared_tags corresponds to a group of tensors to be optimized together.
constant_tags (str, or sequence of str, optional) – If supplied, skip optimizing tensors with any of these tags. This ‘opt-out’ mode is overridden if either tags or shared_tags is supplied.
loss_target (float, optional) – Stop optimizing once this loss value is reached.
optimizer (str, optional) – Which scipy.optimize.minimize optimizer to use (the 'method' kwarg of that function). In addition, quimb implements a few custom optimizers compatible with this interface that you can reference by name - {'adam', 'nadam', 'rmsprop', 'sgd'}.
executor (None or Executor, optional) – To be used with term-by-term Hamiltonians. If supplied, this executor is used to parallelize the evaluation. Otherwise each term is evaluated in sequence. It should implement the basic concurrent.futures (PEP 3148) interface.
progbar (bool, optional) – Whether to show live progress.
bounds (None or (float, float), optional) – Constrain the optimized tensor entries within this range (if the scipy optimizer supports it).
autodiff_backend ({'jax', 'autograd', 'tensorflow', 'torch'}, optional) – Which backend library to use to perform the automatic differentation (and computation).
callback (callable, optional) –
A function to call after each optimization step. It should take the current TNOptimizer instance as its only argument. Information such as the current loss and number of evaluations can then be accessed:
```
def callback(tnopt):
    print(tnopt.nevals, tnopt.loss)
```
backend_opts – Supplied to the backend function compiler and array handler. For example jit_fn=True or device='cpu' .

progbar = True¶

tags = None¶

shared_tags = None¶

constant_tags = None¶

_autodiff_backend = 'AUTO'¶

_multiloss¶

norm_fn = None¶

loss_constants¶

loss_kwargs¶

property bounds¶

property optimizer¶: The underlying optimizer that works with the vectorized functions.

callback = None¶

_set_tn(tn)[source]¶

_reset_tracking_info(loss_target=None)[source]¶

reset(tn=None, clear_info=True, loss_target=None)[source]¶

Reset this optimizer without losing the compiled loss and gradient functions.

Parameters:

tn (TensorNetwork, optional) – Set this tensor network as the current state of the optimizer, it must exactly match the original tensor network.
clear_info (bool, optional) – Clear the tracked losses and iterations.

_maybe_init_pbar(n)[source]¶

_maybe_update_pbar()[source]¶

_maybe_close_pbar()[source]¶

_check_loss_target()[source]¶

_maybe_call_callback()[source]¶

vectorized_value(x)[source]¶: The value of the loss function at vector x.

vectorized_value_and_grad(x)[source]¶: The value and gradient of the loss function at vector x.

vectorized_hessp(x, p)[source]¶: The action of the hessian at point x on vector p.

__repr__()[source]¶

property d¶

property nevals¶: The number of gradient evaluations.

get_tn_opt()[source]¶

Extract the optimized tensor network, this is a three part process:

inject the current optimized vector into the target tensor network or pytree,

run it through norm_fn,

drop any tags used to identify variables.

Returns:: tn_opt
Return type:: TensorNetwork

optimize(n, tol=None, jac=True, hessp=False, optlib='scipy', **options)[source]¶

Run the optimizer for n function evaluations, using by default scipy.optimize.minimize() as the driver for the vectorized computation. Supplying the gradient and hessian vector product is controlled by the jac and hessp options respectively.

Parameters:

n (int) – Notionally the maximum number of iterations for the optimizer, note that depending on the optimizer being used, this may correspond to number of function evaluations rather than just iterations.
tol (None or float, optional) – Tolerance for convergence, note that various more specific tolerances can usually be supplied to options, depending on the optimizer being used.
jac (bool, optional) – Whether to supply the jacobian, i.e. gradient, of the loss function.
hessp (bool, optional) – Whether to supply the hessian vector product of the loss function.
optlib ({'scipy', 'nlopt'}, optional) – Which optimization library to use.
options – Supplied to scipy.optimize.minimize() or whichever optimizer is being used.

Returns:

tn_opt

Return type:

optimize_scipy(n, tol=None, jac=True, hessp=False, **options)[source]¶: Scipy based optimization, see optimize() for details.

optimize_basinhopping(n, nhop, temperature=1.0, jac=True, hessp=False, **options)[source]¶

Run the optimizer for using scipy.optimize.basinhopping() as the driver for the vectorized computation. This performs nhop local optimization each with n iterations.

Parameters:

n (int) – Number of iterations per local optimization.
nhop (int) – Number of local optimizations to hop between.
temperature (float, optional) – H
options – Supplied to the inner scipy.optimize.minimize() call.

Returns:

tn_opt

Return type:

optimize_nlopt(n, tol=None, jac=True, hessp=False, ftol_rel=None, ftol_abs=None, xtol_rel=None, xtol_abs=None)[source]¶

Run the optimizer for n function evaluations, using nlopt as the backend library to run the optimization. Whether the gradient is computed depends on which optimizer is selected, see valid options at https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/.

The following scipy optimizer options are automatically translated to the corresponding nlopt algorithms: {“l-bfgs-b”, “slsqp”, “tnc”, “cobyla”}.

Parameters:

n (int) – The maximum number of iterations for the optimizer.
tol (None or float, optional) – Tolerance for convergence, here this is taken to be the relative tolerance for the loss (ftol_rel below overrides this).
jac (bool, optional) – Whether to supply the jacobian, i.e. gradient, of the loss function.
hessp (bool, optional) – Whether to supply the hessian vector product of the loss function.
ftol_rel (float, optional) – Set relative tolerance on function value.
ftol_abs (float, optional) – Set absolute tolerance on function value.
xtol_rel (float, optional) – Set relative tolerance on optimization parameters.
xtol_abs (float, optional) – Set absolute tolerances on optimization parameters.

Returns:

tn_opt

Return type:

optimize_ipopt(n, tol=None, **options)[source]¶

Run the optimizer for n function evaluations, using ipopt as the backend library to run the optimization via the python package cyipopt.

Parameters:: n (int) – The maximum number of iterations for the optimizer.
Returns:: tn_opt
Return type:: TensorNetwork

optimize_nevergrad(n)[source]¶

Run the optimizer for n function evaluations, using nevergrad as the backend library to run the optimization. As the name suggests, the gradient is not required for this method.

Parameters:: n (int) – The maximum number of iterations for the optimizer.
Returns:: tn_opt
Return type:: TensorNetwork

plot(xscale='symlog', xscale_linthresh=20, zoom='auto', hlines=())[source]¶

Plot the loss function as a function of the number of iterations.

Parameters:

xscale (str, optional) – The scale of the x-axis. Default is "symlog", i.e. linear for the first part of the plot, and logarithmic for the rest, changing at xscale_linthresh.
xscale_linthresh (int, optional) – The threshold for the change from linear to logarithmic scale, if xscale is "symlog". Default is 20.
zoom (None or int, optional) – If not None, show an inset plot of the last zoom iterations.
hlines (dict, optional) – A dictionary of horizontal lines to plot. The keys are the labels of the lines, and the values are the y-values of the lines.

Returns:

fig (matplotlib.figure.Figure) – The figure object.
ax (matplotlib.axes.Axes) – The axes object.

class quimb.tensor.Dense1D(array, phys_dim=2, tags=None, site_ind_id='k{}', site_tag_id='I{}', **tn_opts)[source]¶

Bases: TensorNetwork1DVector

Mimics other 1D tensor network structures, but really just keeps the full state in a single tensor. This allows e.g. applying gates in the same way for quantum circuit simulation as lazily represented hilbert spaces.

Parameters:

array (array_like) – The full hilbert space vector - assumed to be made of equal hilbert spaces each of size phys_dim and will be reshaped as such.
phys_dim (int, optional) – The hilbert space size of each site, default: 2.
tags (sequence of str, optional) – Extra tags to add to the tensor network.
site_ind_id (str, optional) – String formatter describing how to label the site indices.
site_tag_id (str, optional) – String formatter describing how to label the site tags.
tn_opts – Supplied to TensorNetwork.

_EXTRA_PROPS = ('_site_ind_id', '_site_tag_id', '_L')¶

_L¶

_site_ind_id = 'k{}'¶

_site_tag_id = 'I{}'¶

classmethod rand(n, phys_dim=2, dtype=float, **dense1d_opts)[source]¶: Create a random dense vector ‘tensor network’.

class quimb.tensor.MatrixProductOperator(arrays, *, sites=None, L=None, shape='lrud', tags=None, upper_ind_id='k{}', lower_ind_id='b{}', site_tag_id='I{}', **tn_opts)[source]¶

Bases: TensorNetwork1DOperator, TensorNetwork1DFlat

Initialise a matrix product operator, with auto labelling and tagging.

Parameters:

arrays (sequence of arrays) – The tensor arrays to form into a MPO.
sites (sequence of int, optional) – Construct the MPO on these sites only. If not given, enumerate from zero. Should be monotonically increasing and match arrays.
L (int, optional) – The number of sites the MPO should be defined on. If not given, this is taken as the max sites value plus one (i.e.g the number of arrays if sites is not given).
shape (str, optional) – String specifying layout of the tensors. E.g. ‘lrud’ (the default) indicates the shape corresponds left-bond, right-bond, ‘up’ physical index, ‘down’ physical index. End tensors have either ‘l’ or ‘r’ dropped from the string.
tags (str or sequence of str, optional) – Global tags to attach to all tensors.
upper_ind_id (str) – A string specifiying how to label the upper physical site indices. Should contain a '{}' placeholder. It is used to generate the actual indices like: map(upper_ind_id.format, range(len(arrays))).
lower_ind_id (str) – A string specifiying how to label the lower physical site indices. Should contain a '{}' placeholder. It is used to generate the actual indices like: map(lower_ind_id.format, range(len(arrays))).
site_tag_id (str) – A string specifiying how to tag the tensors at each site. Should contain a '{}' placeholder. It is used to generate the actual tags like: map(site_tag_id.format, range(len(arrays))).

_EXTRA_PROPS = ('_site_tag_id', '_upper_ind_id', '_lower_ind_id', 'cyclic', '_L')¶

_L = None¶

_upper_ind_id = 'k{}'¶

_lower_ind_id = 'b{}'¶

_site_tag_id = 'I{}'¶

cyclic¶

classmethod from_fill_fn(fill_fn, L, bond_dim, phys_dim=2, sites=None, cyclic=False, shape='lrud', tags=None, upper_ind_id='k{}', lower_ind_id='b{}', site_tag_id='I{}')[source]¶

Create an MPO by supplying a ‘filling’ function to generate the data for each site.

Parameters:

fill_fn (callable) – A function with signature fill_fn(shape : tuple[int]) -> array_like.
L (int) – The number of sites.
bond_dim (int) – The bond dimension.
phys_dim (int or Sequence[int], optional) – The physical dimension(s) of each site, if a sequence it will be cycled over.
sites (None or sequence of int, optional) – Construct the MPO on these sites only. If not given, enumerate from zero.
cyclic (bool, optional) – Whether the MPO should be cyclic (periodic).
shape (str, optional) – String specifying layout of the tensors. E.g. ‘lrud’ (the default) indicates the shape corresponds left-bond, right-bond, ‘up’ physical index, ‘down’ physical index. End tensors have either ‘l’ or ‘r’ dropped from the string.
tags (str or sequence of str, optional) – Global tags to attach to all tensors.
upper_ind_id (str) – A string specifiying how to label the upper physical site indices. Should contain a '{}' placeholder.
lower_ind_id (str) – A string specifiying how to label the lower physical site indices. Should contain a '{}' placeholder.
site_tag_id (str, optional) – How to tag the physical sites. Should contain a '{}' placeholder.

Return type:

classmethod from_dense(A, dims=2, sites=None, L=None, tags=None, site_tag_id='I{}', upper_ind_id='k{}', lower_ind_id='b{}', **split_opts)[source]¶

Build an MPO from a raw dense matrix.

Parameters:

A (array) – The dense operator, it should be reshapeable to (*dims, *dims).
dims (int, sequence of int, optional) – The physical subdimensions of the operator. If any integer, assume all sites have the same dimension. If a sequence, the dimension of each site. Default is 2.
sites (sequence of int, optional) – The sites to place the operator on. If None, will place it on first len(dims) sites.
L (int, optional) – The total number of sites in the MPO, if the operator represents only a subset.
tags (str or sequence of str, optional) – Global tags to attach to all tensors.
site_tag_id (str, optional) – The string to use to label the site tags.
upper_ind_id (str, optional) – The string to use to label the upper physical indices.
lower_ind_id (str, optional) – The string to use to label the lower physical indices.
split_opts – Supplied to tensor_split().

Return type:

fill_empty_sites(mode='full', phys_dim=None, fill_array=None, inplace=False)[source]¶

Fill any empty sites of this MPO with identity tensors, adding size 1 bonds or draping existing bonds where necessary such that the resulting tensor has nearest neighbor bonds only.

Parameters:

mode ({'full', 'minimal'}, optional) – Whether to fill in all sites, including at either end, or simply the minimal range covering the min to max current sites present.
phys_dim (int, optional) – The physical dimension of the identity tensors to add. If not specified, will use the upper physical dimension of the first present site.
fill_array (array, optional) – The array to use for the identity tensors. If not specified, will use the identity array of the same dtype as the first present site.
inplace (bool, optional) – Whether to perform the operation inplace.

Returns:

The modified MPO.

Return type:

MatrixProductOperator or MatrixProductState

fill_empty_sites_[source]¶

add_MPO(other, inplace=False, **kwargs)[source]¶

add_MPO_[source]¶

_apply_mps(other, compress=False, contract=True, **compress_opts)[source]¶

_apply_mpo(other, compress=False, contract=True, **compress_opts)[source]¶

apply(other, compress=False, **compress_opts)[source]¶

Act with this MPO on another MPO or MPS, such that the resulting object has the same tensor network structure/indices as other.

For an MPS:

       | | | | | | | | | | | | | | | | | |
 self: A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A
       | | | | | | | | | | | | | | | | | |
other: x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x-x

                       -->

       | | | | | | | | | | | | | | | | | |   <- other.site_ind_id
  out: y=y=y=y=y=y=y=y=y=y=y=y=y=y=y=y=y=y

For an MPO:

       | | | | | | | | | | | | | | | | | |
 self: A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A-A
       | | | | | | | | | | | | | | | | | |
other: B-B-B-B-B-B-B-B-B-B-B-B-B-B-B-B-B-B
       | | | | | | | | | | | | | | | | | |

                       -->

       | | | | | | | | | | | | | | | | | |   <- other.upper_ind_id
  out: C=C=C=C=C=C=C=C=C=C=C=C=C=C=C=C=C=C
       | | | | | | | | | | | | | | | | | |   <- other.lower_ind_id

The resulting TN will have the same structure/indices as other, but probably with larger bonds (depending on compression).

Parameters:

other (MatrixProductOperator or MatrixProductState) – The object to act on.
compress (bool, optional) – Whether to compress the resulting object.
compress_opts – Supplied to TensorNetwork1DFlat.compress().

Return type:

dot[source]¶

permute_arrays(shape='lrud')[source]¶

Permute the indices of each tensor in this MPO to match shape. This doesn’t change how the overall object interacts with other tensor networks but may be useful for extracting the underlying arrays consistently. This is an inplace operation.

Parameters:: shape (str, optional) – A permutation of 'lrud' specifying the desired order of the left, right, upper and lower (down) indices respectively.

trace(left_inds=None, right_inds=None)[source]¶: Take the trace of this MPO.

partial_transpose(sysa, inplace=False)[source]¶

Perform the partial transpose on this MPO by swapping the bra and ket indices on sites in sysa.

Parameters:

sysa (sequence of int or int) – The sites to transpose indices on.
inplace (bool, optional) – Whether to perform the partial transposition inplace.

Return type:

rand_state(bond_dim, **mps_opts)[source]¶: Get a random vector matching this MPO.

identity(**mpo_opts)[source]¶: Get a identity matching this MPO.

show(max_width=None)[source]¶

class quimb.tensor.MatrixProductState(arrays, *, sites=None, L=None, shape='lrp', tags=None, site_ind_id='k{}', site_tag_id='I{}', **tn_opts)[source]¶

Bases: TensorNetwork1DVector, TensorNetwork1DFlat

Initialise a matrix product state, with auto labelling and tagging.

Parameters:

arrays (sequence of arrays) – The tensor arrays to form into a MPS.
sites (sequence of int, optional) – Construct the MPO on these sites only. If not given, enumerate from zero. Should be monotonically increasing and match arrays.
L (int, optional) – The number of sites the MPO should be defined on. If not given, this is taken as the max sites value plus one (i.e.g the number of arrays if sites is not given).
shape (str, optional) – String specifying layout of the tensors. E.g. ‘lrp’ (the default) indicates the shape corresponds left-bond, right-bond, physical index. End tensors have either ‘l’ or ‘r’ dropped from the string.
tags (str or sequence of str, optional) – Global tags to attach to all tensors.
site_ind_id (str) – A string specifiying how to label the physical site indices. Should contain a '{}' placeholder. It is used to generate the actual indices like: map(site_ind_id.format, range(len(arrays))).
site_tag_id (str) – A string specifiying how to tag the tensors at each site. Should contain a '{}' placeholder. It is used to generate the actual tags like: map(site_tag_id.format, range(len(arrays))).

_EXTRA_PROPS = ('_site_tag_id', '_site_ind_id', 'cyclic', '_L')¶

_L¶

_site_ind_id = 'k{}'¶

_site_tag_id = 'I{}'¶

cyclic¶

classmethod from_fill_fn(fill_fn, L, bond_dim, phys_dim=2, sites=None, cyclic=False, shape='lrp', site_ind_id='k{}', site_tag_id='I{}', tags=None)[source]¶

Create an MPS by supplying a ‘filling’ function to generate the data for each site.

Parameters:

fill_fn (callable) – A function with signature fill_fn(shape : tuple[int]) -> array_like.
L (int) – The number of sites.
bond_dim (int) – The bond dimension.
phys_dim (int or Sequence[int], optional) – The physical dimension(s) of each site, if a sequence it will be cycled over.
sites (None or sequence of int, optional) – Construct the MPS on these sites only. If not given, enumerate from zero.
cyclic (bool, optional) – Whether the MPS should be cyclic (periodic).
shape (str, optional) – What specific order to layout the indices in, should be a sequence of 'l', 'r', and 'p', corresponding to left, right, and physical indices respectively.
site_ind_id (str, optional) – How to label the physical site indices.
site_tag_id (str, optional) – How to tag the physical sites.
tags (str or sequence of str, optional) – Global tags to attach to all tensors.

Return type:

classmethod from_dense(psi, dims=2, tags=None, site_ind_id='k{}', site_tag_id='I{}', **split_opts)[source]¶

Create a MatrixProductState directly from a dense vector

Parameters:

psi (array_like) – The dense state to convert to MPS from.
dims (int or sequence of int) – Physical subsystem dimensions of each site. If a single int, all sites have this same dimension, by default, 2.
tags (str or sequence of str, optional) – Global tags to attach to all tensors.
site_ind_id (str, optional) – How to index the physical sites, see MatrixProductState.
site_tag_id (str, optional) – How to tag the physical sites, see MatrixProductState.
split_opts – Supplied to tensor_split() to in order to partition the dense vector into tensors. absorb='left' is set by default, to ensure the compression is canonical / optimal.

Return type:

Examples

>>> dims = [2, 2, 2, 2, 2, 2]
>>> psi = rand_ket(prod(dims))
>>> mps = MatrixProductState.from_dense(psi, dims)
>>> mps.show()
 2 4 8 4 2
o-o-o-o-o-o
| | | | | |

add_MPS(other, inplace=False, **kwargs)[source]¶: Add another MatrixProductState to this one.

add_MPS_[source]¶

permute_arrays(shape='lrp')[source]¶

Permute the indices of each tensor in this MPS to match shape. This doesn’t change how the overall object interacts with other tensor networks but may be useful for extracting the underlying arrays consistently. This is an inplace operation.

Parameters:: shape (str, optional) – A permutation of 'lrp' specifying the desired order of the left, right, and physical indices respectively.

normalize(bra=None, eps=1e-15, insert=None)[source]¶

Normalize this MPS, optional with co-vector bra. For periodic MPS this uses transfer matrix SVD approximation with precision eps in order to be efficient. Inplace.

Parameters:

bra (MatrixProductState, optional) – If given, normalize this MPS with the same factor.
eps (float, optional) – If cyclic, precision to approximation transfer matrix with. Default: 1e-14.
insert (int, optional) – Insert the corrective normalization on this site, random if not given.

Returns:

old_norm – The old norm self.H @ self.

Return type:

gate_split(G, where, inplace=False, **compress_opts)[source]¶

Apply a two-site gate and then split resulting tensor to retrieve a MPS form:

-o-o-A-B-o-o-
 | | | | | |            -o-o-GGG-o-o-           -o-o-X~Y-o-o-
 | | GGG | |     ==>     | | | | | |     ==>     | | | | | |
 | | | | | |                 i j                     i j
     i j

As might be found in TEBD.

Parameters:

G (array) – The gate, with shape (d**2, d**2) for physical dimension d.
where ((int, int)) – Indices of the sites to apply the gate to.
compress_opts – Supplied to tensor_split().

See also

gate, gate_with_auto_swap

gate_split_[source]¶

swap_sites_with_compress(i, j, info=None, inplace=False, **compress_opts)[source]¶

Swap sites i and j by contracting, then splitting with the physical indices swapped. If the sites are not adjacent, this will happen multiple times.

Parameters:

i (int) – The first site to swap.
j (int) – The second site to swap.
cur_orthog (int, sequence of int, or 'calc') – If known, the current orthogonality center.
info (dict, optional) – If supplied, will be used to infer and store various extra information. Currently, the key “cur_orthog” is used to store the current orthogonality center. Its input value can be "calc", a single site, or a pair of sites representing the min/max range, inclusive. It will be updated to the actual range after.
inplace (bond, optional) – Perform the swaps inplace.
compress_opts – Supplied to tensor_split().

swap_sites_with_compress_[source]¶

swap_site_to(i, f, info=None, inplace=False, **compress_opts)[source]¶

Swap site i to site f, compressing the bond after each swap:

      i       f
0 1 2 3 4 5 6 7 8 9      0 1 2 4 5 6 7 3 8 9
o-o-o-x-o-o-o-o-o-o      >->->->->->->-x-<-<
| | | | | | | | | |  ->  | | | | | | | | | |

Parameters:

i (int) – The site to move.
f (int) – The new location for site i.
info (dict, optional) – If supplied, will be used to infer and store various extra information. Currently, the key “cur_orthog” is used to store the current orthogonality center. Its input value can be "calc", a single site, or a pair of sites representing the min/max range, inclusive. It will be updated to the actual range after.
inplace (bond, optional) – Perform the swaps inplace.
compress_opts – Supplied to tensor_split().

swap_site_to_[source]¶

gate_with_auto_swap(G, where, info=None, swap_back=True, inplace=False, **compress_opts)[source]¶

Perform a two site gate on this MPS by, if necessary, swapping and compressing the sites until they are adjacent, using gate_split, then unswapping the sites back to their original position.

Parameters:

G (array) – The gate, with shape (d**2, d**2) for physical dimension d.
where ((int, int)) – Indices of the sites to apply the gate to.
info (dict, optional) – If supplied, will be used to infer and store various extra information. Currently, the key “cur_orthog” is used to store the current orthogonality center. Its input value can be "calc", a single site, or a pair of sites representing the min/max range, inclusive. It will be updated to the actual range after.
swap_back (bool, optional) – Whether to swap the sites back to their original position after applying the gate. If not, for sites i < j, the site j will remain swapped to i + 1, and sites between i + 1 and j will be shifted one place up.
inplace (bond, optional) – Perform the swaps inplace.
compress_opts – Supplied to tensor_split().

See also

gate, gate_split

gate_with_auto_swap_[source]¶

gate_with_submpo(submpo, where=None, method='direct', transpose=False, info=None, inplace=False, inplace_mpo=False, **compress_opts)[source]¶

Apply an MPO, which only acts on a subset of sites, to this MPS, compressing the MPS with the MPO only on the minimal set of sites covering where, keeping the MPS form:

    │   │ │
    A───A─A
    │   │ │         ->    │ │ │ │ │ │ │ │
                          >─>─O━O━O━O─<─<
│ │ │ │ │ │ │ │
o─o─o─o─o─o─o─o

Parameters:

submpo (MatrixProductOperator) – The MPO to apply.
where (sequence of int, optional) – The range of sites the MPO acts on, will be inferred from the support of the MPO if not given.
method ({'direct", 'dm', 'zipup', 'zipup-first', 'fit'}, optional) – The compression method to use.
transpose (bool, optional) – Whether to transpose the MPO before applying it. By default the lower inds of the MPO are contracted with the MPS, if transposed the upper inds are contracted.
info (dict, optional) – If supplied, will be used to infer and store various extra information. Currently, the key “cur_orthog” is used to store the current orthogonality center. Its input value can be "calc", a single site, or a pair of sites representing the min/max range, inclusive. It will be updated to the actual range after.
inplace (bool, optional) – Whether to perform the application and compression inplace.
compress_opts – Supplied to tensor_network_1d_compress().

Return type:

gate_with_submpo_[source]¶

gate_with_mpo(mpo, method='direct', transpose=False, inplace=False, inplace_mpo=False, **compress_opts)[source]¶

Gate this MPS with an MPO and compress the result with one of various methods back to MPS form:

│ │ │ │ │ │ │ │
A─A─A─A─A─A─A─A
│ │ │ │ │ │ │ │     ->    │ │ │ │ │ │ │ │
                          O━O━O━O━O━O━O━O
│ │ │ │ │ │ │ │
o─o─o─o─o─o─o─o

Parameters:

mpo (MatrixProductOperator) – The MPO to apply.
max_bond (int, optional) – A maximum bond dimension to keep when compressing.
cutoff (float, optional) – A singular value cutoff to use when compressing.
method ({'direct", 'dm', 'zipup', 'zipup-first', 'fit', ...}, optional) – The compression method to use.
transpose (bool, optional) – Whether to transpose the MPO before applying it. By default the lower inds of the MPO are contracted with the MPS, if transposed the upper inds are contracted.
inplace (bool, optional) – Whether to perform the compression inplace.
inplace_mpo (bool, optional) – Whether the modify the MPO in place, a minor performance gain.
compress_opts – Other options supplied to tensor_network_1d_compress().

Return type:

gate_with_mpo_[source]¶

gate_nonlocal(G, where, dims=None, method='direct', info=None, inplace=False, **compress_opts)[source]¶

Apply a potentially non-local gate to this MPS by first decomposing it into an MPO, then compressing the MPS with MPO only on the minimal set of sites covering where.

Parameters:

G (array_like) – The gate to apply.
where (sequence of int) – The sites to apply the gate to.
max_bond (int, optional) – A maximum bond dimension to keep when compressing.
cutoff (float, optional) – A singular value cutoff to use when compressing.
dims (sequence of int, optional) – The factorized dimensions of the gate G, which should match the physical dimensions of the sites it acts on. Calculated if not supplied. If a single int, all sites are assumed to have this same dimension.
method ({'direct", 'dm', 'zipup', 'zipup-first', 'fit', ...}, optional) – The compression method to use.
info (dict, optional) – If supplied, will be used to infer and store various extra information. Currently, the key “cur_orthog” is used to store the current orthogonality center. Its input value can be "calc", a single site, or a pair of sites representing the min/max range, inclusive. It will be updated to the actual range after.
inplace (bool, optional) – Whether to perform the compression inplace.
compress_opts – Supplied to tensor_network_1d_compress().

Return type:

gate_nonlocal_[source]¶

flip(inplace=False)[source]¶: Reverse the order of the sites in the MPS, such that site i is now at site L - i - 1.

magnetization(i, direction='Z', info=None)[source]¶: Compute the magnetization at site i.

schmidt_values(i, info=None, method='svd')[source]¶

Find the schmidt values associated with the bipartition of this MPS between sites on either site of i. In other words, i is the number of sites in the left hand partition:

....L....   i
o-o-o-o-o-S-o-o-o-o-o-o-o-o-o-o-o
| | | | |   | | | | | | | | | | |
       i-1  ..........R..........

The schmidt values, S, are the singular values associated with the (i - 1, i) bond, squared, provided the MPS is mixed canonized at one of those sites.

Parameters:

i (int) – The number of sites in the left partition.
info (dict, optional) – If given, will be used to infer and store various extra information. Currently the key “cur_orthog” is used to store the current orthogonality center.

Returns:

S – The schmidt values.

Return type:

1d-array

entropy(i, info=None, method='svd')[source]¶

The entropy of bipartition between the left block of i sites and the rest.

Parameters:

i (int) – The number of sites in the left partition.
info (dict, optional) – If given, will be used to infer and store various extra information. Currently the key “cur_orthog” is used to store the current orthogonality center.

Return type:

schmidt_gap(i, info=None, method='svd')[source]¶

The schmidt gap of bipartition between the left block of i sites and the rest.

Parameters:

i (int) – The number of sites in the left partition.
info (dict, optional) – If given, will be used to infer and store various extra information. Currently the key “cur_orthog” is used to store the current orthogonality center.

Return type:

partial_trace_to_mpo(keep, upper_ind_id='b{}', rescale_sites=True)[source]¶

Partially trace this matrix product state, producing a matrix product operator.

Parameters:

keep (sequence of int or slice) – Indicies of the sites to keep.
upper_ind_id (str, optional) – The ind id of the (new) ‘upper’ inds, i.e. the ‘bra’ inds.
rescale_sites (bool, optional) – If True (the default), then the kept sites will be rescaled to (0, 1, 2, ...) etc. rather than keeping their original site numbers.

Returns:

rho – The density operator in MPO form.

Return type:

partial_trace(*_, **__)[source]¶

Partially trace this tensor network state, keeping only the sites in keep, using compressed contraction.

Parameters:

keep (iterable of hashable) – The sites to keep.
max_bond (int) – The maximum bond dimensions to use while compressed contracting.
optimize (str or PathOptimizer, optional) – The contraction path optimizer to use, should specifically generate contractions paths designed for compressed contraction.
flatten ({False, True, 'all'}, optional) – Whether to force ‘flattening’ (contracting all physical indices) of the tensor network before contraction, whilst this makes the TN generally more complex to contract, the accuracy is usually improved. If 'all' also flatten the tensors in keep.
reduce (bool, optional) – Whether to first ‘pull’ the physical indices off their respective tensors using QR reduction. Experimental.
normalized (bool, optional) – Whether to normalize the reduced density matrix at the end.
symmetrized ({'auto', True, False}, optional) – Whether to symmetrize the reduced density matrix at the end. This should be unecessary if flatten is set to True.

rehearse ({False, 'tn', 'tree', True}, optional) –

Whether to perform the computation or not:

- False: perform the computation.
- 'tn': return the tensor network without running the path
  optimizer.
- 'tree': run the path optimizer and return the
  ``cotengra.ContractonTree``..
- True: run the path optimizer and return the ``PathInfo``.

contract_compressed_opts (dict, optional) – Additional keyword arguments to pass to contract_compressed().

Returns:

rho – The reduce density matrix of sites in keep.

Return type:

array_like

ptr(*_, **__)[source]¶

partial_trace_to_dense_canonical(where, normalized=True, info=None, **contract_opts)[source]¶

Compute the dense local reduced density matrix by canonicalizing around the target sites and then contracting the local tensors. Note this moves the orthogonality around inplace, and records it in info.

Parameters:

where (int or tuple[int]) – The site or sites to compute the reduced density matrix for.
normalized (bool, optional) – Explicitly normalize the local reduced density matrix.
info (dict, optional) – If supplied, will be used to infer and store various extra information. Currently the key “cur_orthog” is used to store the current orthogonality center. Its input value can be "calc", a single site, or a pair of sites representing the min/max range, inclusive. It will be updated to the actual range after.
contract_opts – Passed to tensor_contract when computing the reduced local density matrix.

Return type:

array_like

local_expectation_canonical(G, where, normalized=True, info=None, **contract_opts)[source]¶

Compute a local expectation value (via forming the reduced density matrix). Note this moves the orthogonality around inplace, and records it in info.

Parameters:

G (array_like) – The local operator to compute the expectation of.
where (int or tuple[int]) – The site or sites to compute the expectation at.
normalized (bool, optional) – Explicitly normalize the local reduced density matrix.
info (dict, optional) – If supplied, will be used to infer and store various extra information. Currently the key “cur_orthog” is used to store the current orthogonality center. Its input value can be "calc", a single site, or a pair of sites representing the min/max range, inclusive. It will be updated to the actual range after.
contract_opts – Passed to tensor_contract when computing the reduced local density matrix.

Return type:

compute_local_expectation_canonical(terms, normalized=True, return_all=False, info=None, inplace=False, **contract_opts)[source]¶

Compute many local expectations at once, via forming the relevant reduced density matrices via canonicalization. This moves the orthogonality around inplace, and records it in info.

Parameters:

terms (dict[int or tuple[int], array_like]) – The local terms to compute values for.
normalized (bool, optional) – Explicitly normalize each local reduced density matrix.
return_all (bool, optional) – Whether to return each expectation in terms separately or sum them all together (the default).
info (dict, optional) – If supplied, will be used to infer and store various extra information. Currently, the key “cur_orthog” is used to store the current orthogonality center. Its input value can be "calc", a single site, or a pair of sites representing the min/max range, inclusive. It will be updated to the actual range after.
inplace (bool, optional) – Whether to perform the required canonicalizations inplace.
contract_opts – Supplied to contract() when contracting the local density matrices.

Returns:

The expecetation value(s), either summed or for each term if return_all=True.

Return type:

float or dict[in or tuple[int], float]

compute_local_expectation_via_envs(terms, normalized=True, return_all=False, **contract_opts)[source]¶

Compute many local expectations at once, via forming the relevant local overlaps using left and right environments formed via contraction. This does not require any canonicalization and can be quicker if the canonical center is not already aligned.

Parameters:

terms (dict[int or tuple[int], array_like]) – The local terms to compute values for.
normalized (bool, optional) – Explicitly normalize each local reduced density matrix.
return_all (bool, optional) – Whether to return each expectation in terms separately or sum them all together (the default).
contract_opts – Supplied to contract() when contracting the local overlaps.

Returns:

The expecetation value(s), either summed or for each term if return_all=True.

Return type:

float or dict[int or tuple[int], float]

See also

compute_local_expectation_canonical, compute_left_environments, compute_right_environments

compute_local_expectation(terms, normalized=True, return_all=False, method='canonical', info=None, inplace=False, **contract_opts)[source]¶

Compute many local expectations at once.

Parameters:

terms (dict[int or tuple[int], array_like]) – The local terms to compute values for.
normalized (bool, optional) – Explicitly normalize each local term.
return_all (bool, optional) – Whether to return each expectation in terms separately or sum them all together (the default).
method ({'canonical', 'envs'}, optional) –
The method to use to compute the local expectations.
- ’canonical’: canonicalize around the sites of interest and contract the local reduced density matrices, moving the canonical center around as needed.
- ’envs’: form the local overlaps using left and right environments and contract these directly. This can be quicker if the canonical center is not already aligned.
info (dict, optional) – If supplied, and method==”canonical”, will be used to infer and store various extra information. Currently the key “cur_orthog” is used to store the current orthogonality center. Its input value can be "calc", a single site, or a pair of sites representing the min/max range, inclusive. It will be updated to the actual range after.
inplace (bool, optional) – If method==”canonical”, whether to perform the required canonicalizations inplace or on a copy of the state.
contract_opts – Supplied to contract() when contracting the local overlaps or density matrices.

Returns:

The expecetation value(s), either summed or for each term if return_all=True.

Return type:

float or dict[int or tuple[int], float]

bipartite_schmidt_state(sz_a, get='ket', info=None)[source]¶

Compute the reduced state for a bipartition of an OBC MPS, in terms of the minimal left/right schmidt basis:

     A            B
 .........     ...........
 >->->->->--s--<-<-<-<-<-<    ->   +-s-+
 | | | | |     | | | | | |         |   |
k0 k1...                          kA   kB

Parameters:

sz_a (int) – The number of sites in subsystem A, must be 0 < sz_a < N.
get ({'ket', 'rho', 'ket-dense', 'rho-dense'}, optional) –
Get the:
- ’ket’: vector form as tensor.
- ’rho’: density operator form, i.e. vector outer product
- ’ket-dense’: like ‘ket’ but return qarray.
- ’rho-dense’: like ‘rho’ but return qarray.
info (dict, optional) – If given, will be used to infer and store various extra information. Currently the key “cur_orthog” is used to store the current orthogonality center.

static _do_lateral_compress(mps, kb, section, leave_short, ul, ll, heps, hmethod, hmax_bond, verbosity, compressed, **compress_opts)[source]¶

static _do_vertical_decomp(mps, kb, section, sysa, sysb, compressed, ul, ur, ll, lr, vmethod, vmax_bond, veps, verbosity, **compress_opts)[source]¶

partial_trace_compress(sysa, sysb, eps=1e-08, method=('isvd', None), max_bond=(None, 1024), leave_short=True, renorm=True, lower_ind_id='b{}', verbosity=0, **compress_opts)[source]¶

Perform a compressed partial trace using singular value lateral then vertical decompositions of transfer matrix products:

        .....sysa......     ...sysb....
o-o-o-o-A-A-A-A-A-A-A-A-o-o-B-B-B-B-B-B-o-o-o-o-o-o-o-o-o
| | | | | | | | | | | | | | | | | | | | | | | | | | | | |

                          ==> form inner product

        ...............     ...........
o-o-o-o-A-A-A-A-A-A-A-A-o-o-B-B-B-B-B-B-o-o-o-o-o-o-o-o-o
| | | | | | | | | | | | | | | | | | | | | | | | | | | | |
o-o-o-o-A-A-A-A-A-A-A-A-o-o-B-B-B-B-B-B-o-o-o-o-o-o-o-o-o

                          ==> lateral SVD on each section

          .....sysa......     ...sysb....
          /\             /\   /\         /\
  ... ~~~E  A~~~~~~~~~~~A  E~E  B~~~~~~~B  E~~~ ...
          \/             \/   \/         \/

                          ==> vertical SVD and unfold on A & B

                  |                 |
          /-------A-------\   /-----B-----\
  ... ~~~E                 E~E             E~~~ ...
          \-------A-------/   \-----B-----/
                  |                 |

With various special cases including OBC or end spins included in subsytems.

Parameters:

sysa (sequence of int) – The sites, which should be contiguous, defining subsystem A.
sysb (sequence of int) – The sites, which should be contiguous, defining subsystem B.
eps (float or (float, float), optional) – Tolerance(s) to use when compressing the subsystem transfer matrices and vertically decomposing.
method (str or (str, str), optional) – Method(s) to use for laterally compressing the state then vertially compressing subsytems.
max_bond (int or (int, int), optional) – The maximum bond to keep for laterally compressing the state then vertially compressing subsytems.
leave_short (bool, optional) – If True (the default), don’t try to compress short sections.
renorm (bool, optional) – If True (the default), renomalize the state so that tr(rho)==1.
lower_ind_id (str, optional) – The index id to create for the new density matrix, the upper_ind_id is automatically taken as the current site_ind_id.
compress_opts (dict, optional) – If given, supplied to partial_trace_compress to govern how singular values are treated. See tensor_split.
verbosity ({0, 1}, optional) – How much information to print while performing the compressed partial trace.

Returns:

rho_ab – Density matrix tensor network with outer_inds = ('k0', 'k1', 'b0', 'b1') for example.

Return type:

logneg_subsys(sysa, sysb, compress_opts=None, approx_spectral_opts=None, verbosity=0, approx_thresh=2**12)[source]¶

Compute the logarithmic negativity between subsytem blocks, e.g.:

                   sysa         sysb
                 .........       .....
... -o-o-o-o-o-o-A-A-A-A-A-o-o-o-B-B-B-o-o-o-o-o-o-o- ...
     | | | | | | | | | | | | | | | | | | | | | | | |

Parameters:

sysa (sequence of int) – The sites, which should be contiguous, defining subsystem A.
sysb (sequence of int) – The sites, which should be contiguous, defining subsystem B.
eps (float, optional) – Tolerance to use when compressing the subsystem transfer matrices.
method (str or (str, str), optional) – Method(s) to use for laterally compressing the state then vertially compressing subsytems.
compress_opts (dict, optional) – If given, supplied to partial_trace_compress to govern how singular values are treated. See tensor_split.
approx_spectral_opts – Supplied to approx_spectral_function().

Returns:

ln – The logarithmic negativity.

Return type:

See also

MatrixProductState.partial_trace_compress, approx_spectral_function

measure(site, remove=False, outcome=None, renorm=True, info=None, get=None, seed=None, inplace=False)[source]¶

Measure this MPS at site, including projecting the state. Optionally remove the site afterwards, yielding an MPS with one less site. In either case the orthogonality center of the returned MPS is min(site, new_L - 1).

Parameters:

site (int) – The site to measure.
remove (bool, optional) –
Whether to remove the site completely after projecting the measurement. If True, sites greater than site will be retagged and reindex one down, and the MPS will have one less site. E.g:
```
0-1-2-3-4-5-6
       / / /  - measure and remove site 3
0-1-2-4-5-6
              - reindex sites (4, 5, 6) to (3, 4, 5)
0-1-2-3-4-5
```
outcome (None or int, optional) – Specify the desired outcome of the measurement. If None, it will be randomly sampled according to the local density matrix.
renorm (bool, optional) – Whether to renormalize the state post measurement.
info (dict, optional) – If given, will be used to infer and store various extra information. Currently the key “cur_orthog” is used to store the current orthogonality center.
get ({None, 'outcome'}, optional) – If 'outcome', simply return the outcome, and don’t perform any projection.
seed (None, int, or np.random.Generator, optional) – A random seed or generator to use.
inplace (bool, optional) – Whether to perform the measurement in place or not.

Returns:

outcome (int) – The measurement outcome, drawn from range(phys_dim).
psi (MatrixProductState) – The measured state, if get != 'outcome'.

measure_[source]¶

sample_configuration(seed=None, info=None)[source]¶

Sample a configuration from this MPS.

Parameters:

seed (None, int, or np.random.Generator, optional) – A random seed or generator to use.
info (dict, optional) – If given, will be used to infer and store various extra information. Currently the key “cur_orthog” is used to store the current orthogonality center.

sample(C, seed=None, info=None)[source]¶

Generate C samples rom this MPS, along with their probabilities.

Parameters:

C (int) – The number of samples to generate.
seed (None, int, or np.random.Generator, optional) – A random seed or generator to use.
info (dict, optional) – If given, will be used to infer and store various extra information. Currently the key “cur_orthog” is used to store the current orthogonality center.

Yields:

config (sequence of int) – The sample configuration.
omega (float) – The probability of this configuration.

class quimb.tensor.SuperOperator1D(arrays, shape='lrkud', site_tag_id='I{}', outer_upper_ind_id='kn{}', inner_upper_ind_id='k{}', inner_lower_ind_id='b{}', outer_lower_ind_id='bn{}', tags=None, tags_upper=None, tags_lower=None, **tn_opts)[source]¶

Bases: TensorNetwork1D

A 1D tensor network super-operator class:

0   1   2       n-1
|   |   |        |     <-- outer_upper_ind_id
O===O===O==     =O
|\  |\  |\       |\     <-- inner_upper_ind_id
  )   )   ) ...    )   <-- K (size of local Kraus sum)
|/  |/  |/       |/     <-- inner_lower_ind_id
O===O===O==     =O
|   | : |        |     <-- outer_lower_ind_id
      :
     chi (size of entangling bond dim)

Parameters:: arrays (sequence of arrays) – The data arrays defining the superoperator, this should be a sequence of 2n arrays, such that the first two correspond to the upper and lower operators acting on site 0 etc. The arrays should be 5 dimensional unless OBC conditions are desired, in which case the first two and last two should be 4-dimensional. The dimensions of array can be should match the shape option.

_EXTRA_PROPS = ('_site_tag_id', '_outer_upper_ind_id', '_inner_upper_ind_id', '_inner_lower_ind_id',...¶

_L¶

_outer_upper_ind_id = 'kn{}'¶

_inner_upper_ind_id = 'k{}'¶

_inner_lower_ind_id = 'b{}'¶

_outer_lower_ind_id = 'bn{}'¶

_site_tag_id = 'I{}'¶

cyclic¶

classmethod rand(n, K, chi, phys_dim=2, herm=True, cyclic=False, dtype=complex, **superop_opts)[source]¶

property outer_upper_ind_id¶

property inner_upper_ind_id¶

property inner_lower_ind_id¶

property outer_lower_ind_id¶

class quimb.tensor.TensorNetwork1D(ts=(), *, virtual=False, check_collisions=True)[source]¶

Bases: quimb.tensor.tensor_arbgeom.TensorNetworkGen

Base class for tensor networks with a one-dimensional structure.

_NDIMS = 1¶

_EXTRA_PROPS = ('_site_tag_id', '_L')¶

_CONTRACT_STRUCTURED = True¶

_compatible_1d(other)[source]¶: Check whether self and other are compatible 2D tensor networks such that they can remain a 2D tensor network when combined.

combine(other, *, virtual=False, check_collisions=True)[source]¶

Combine this tensor network with another, returning a new tensor network. If the two are compatible, cast the resulting tensor network to a TensorNetwork1D instance.

Parameters:

other (TensorNetwork1D or TensorNetwork) – The other tensor network to combine with.
virtual (bool, optional) – Whether the new tensor network should copy all the incoming tensors (False, the default), or view them as virtual (True).
check_collisions (bool, optional) – Whether to check for index collisions between the two tensor networks before combining them. If True (the default), any inner indices that clash will be mangled.

Return type:

TensorNetwork1D or TensorNetwork

property L¶: The number of sites, i.e. length.

property nsites¶: The number of sites.

gen_site_coos()[source]¶: Generate the coordinates of all possible sites.

site_tag(i)[source]¶: The name of the tag specifiying the tensor at site i.

slice2sites(tag_slice)[source]¶

Take a slice object, and work out its implied start, stop and step, taking into account cyclic boundary conditions.

Examples

Normal slicing:

>>> p = MPS_rand_state(10, bond_dim=7)
>>> p.slice2sites(slice(5))
(0, 1, 2, 3, 4)

>>> p.slice2sites(slice(4, 8))
(4, 5, 6, 7)

Slicing from end backwards:

>>> p.slice2sites(slice(..., -3, -1))
(9, 8)

Slicing round the end:

>>> p.slice2sites(slice(7, 12))
(7, 8, 9, 0, 1)

>>> p.slice2sites(slice(-3, 2))
(7, 8, 9, 0, 1)

If the start point is > end point (before modulo n), then step needs to be negative to return anything.

maybe_convert_coo(x)[source]¶: Check if x is an integer and convert to the corresponding site tag if so.

contract_structured(tag_slice, structure_bsz=5, inplace=False, **opts)[source]¶

Perform a structured contraction, translating tag_slice from a slice or … to a cumulative sequence of tags.

Parameters:

tag_slice (slice or ...) – The range of sites, or … for all.
inplace (bool, optional) – Whether to perform the contraction inplace.

Returns:

The result of the contraction, still a TensorNetwork if the contraction was only partial.

Return type:

TensorNetwork, Tensor or scalar

See also

contract, contract_tags, contract_cumulative

compute_left_environments(**contract_opts)[source]¶

Compute the left environments of this 1D tensor network.

Parameters:: contract_opts – Supplied to contract().
Returns:: Environments indexed by the site they are to the left of, so keys run from (1, … L - 1).
Return type:: dict[int, Tensor]

compute_right_environments(**contract_opts)[source]¶

Compute the right environments of this 1D tensor network.

Parameters:: contract_opts – Supplied to contract().
Returns:: Environments indexed by the site they are to the right of, so keys run from (0, … L - 2).
Return type:: dict[int, Tensor]

_repr_info()[source]¶: General info to show in various reprs. Sublasses can add more relevant info to this dict.

class quimb.tensor.TNLinearOperator1D(tn, left_inds, right_inds, start, stop, ldims=None, rdims=None, is_conj=False, is_trans=False)[source]¶

Bases: scipy.sparse.linalg.LinearOperator

A 1D tensor network linear operator like:

         start                 stop - 1
           .                     .
         :-O-O-O-O-O-O-O-O-O-O-O-O-:                 --+
         : | | | | | | | | | | | | :                   |
         :-H-H-H-H-H-H-H-H-H-H-H-H-:    acting on    --V
         : | | | | | | | | | | | | :                   |
         :-O-O-O-O-O-O-O-O-O-O-O-O-:                 --+
left_inds^                         ^right_inds

Like TNLinearOperator, but performs a structured contract from one end to the other than can handle very long chains possibly more efficiently by contracting in blocks from one end.

Parameters:

tn (TensorNetwork) – The tensor network to turn into a LinearOperator.
left_inds (sequence of str) – The left indicies.
right_inds (sequence of str) – The right indicies.
start (int) – Index of starting site.
stop (int) – Index of stopping site (does not include this site).
ldims (tuple of int, optional) – If known, the dimensions corresponding to left_inds.
rdims (tuple of int, optional) – If known, the dimensions corresponding to right_inds.

See also

TNLinearOperator

tn¶

tags¶

is_conj = False¶

is_trans = False¶

_conj_linop = None¶

_adjoint_linop = None¶

_transpose_linop = None¶

_matvec(vec)[source]¶

Default matrix-vector multiplication handler.

If self is a linear operator of shape (M, N), then this method will be called on a shape (N,) or (N, 1) ndarray, and should return a shape (M,) or (M, 1) ndarray.

This default implementation falls back on _matmat, so defining that will define matrix-vector multiplication as well.

_matmat(mat)[source]¶

Default matrix-matrix multiplication handler.

Falls back on the user-defined _matvec method, so defining that will define matrix multiplication (though in a very suboptimal way).

copy(conj=False, transpose=False)[source]¶

conj()[source]¶

_transpose()[source]¶: Default implementation of _transpose; defers to rmatvec + conj

_adjoint()[source]¶: Hermitian conjugate of this TNLO.

to_dense()[source]¶

toarray()[source]¶

property A¶

quimb.tensor.align_TN_1D[source]¶

quimb.tensor.expec_TN_1D(*tns, compress=None, eps=1e-15)[source]¶

Compute the expectation of several 1D TNs, using transfer matrix compression if any are periodic.

Parameters:

tns (sequence of TensorNetwork1D) – The MPS and MPO to find expectation of. Should start and begin with an MPS e.g. (MPS, MPO, ..., MPS).
compress ({None, False, True}, optional) – Whether to perform transfer matrix compression on cyclic systems. If set to None (the default), decide heuristically.
eps (float, optional) – The accuracy of the transfer matrix compression.

Returns:

x – The expectation value.

Return type:

MatrixProductState.gate_split

quimb.tensor.gate_TN_1D(tn, G, where, contract=False, tags=None, propagate_tags='sites', info=None, inplace=False, cur_orthog=None, **compress_opts)[source]¶

Act with the gate G on sites where, maintaining the outer indices of the 1D tensor network:

contract=False       contract=True
    . .                    . .             <- where
o-o-o-o-o-o-o        o-o-o-GGG-o-o-o
| | | | | | |        | | | / \ | | |
    GGG
    | |


contract='split-gate'        contract='swap-split-gate'
      . .                          . .                      <- where
  o-o-o-o-o-o-o                o-o-o-o-o-o-o
  | | | | | | |                | | | | | | |
      G~G                          G~G
      | |                          \ /
                                    X
                                   / \

contract='swap+split'
        . .            <- where
  o-o-o-G=G-o-o-o
  | | | | | | | |

Note that the sites in where do not have to be contiguous. By default, site tags will be propagated to the gate tensors, identifying a ‘light cone’.

Parameters:

tn (TensorNetwork1DVector) – The 1D vector-like tensor network, for example, and MPS.
G (array) – A square array to act with on sites where. It should have twice the number of dimensions as the number of sites. The second half of these will be contracted with the MPS, and the first half indexed with the correct site_ind_id. Sites are read left to right from the shape. A two-dimensional array is permissible if each dimension factorizes correctly.
where (int or sequence of int) – Where the gate should act.
contract ({False, 'split-gate', 'swap-split-gate',) –
‘auto-split-gate’, True, ‘swap+split’}, optional Whether to contract the gate into the 1D tensor network. If,
- False: leave the gate uncontracted, the default
- ’split-gate’: like False, but split the gate if it is two-site.
- ’swap-split-gate’: like ‘split-gate’, but decompose the gate as if a swap had first been applied
- ’auto-split-gate’: automatically select between the above three options, based on the rank of the gate.
- True: contract the gate into the tensor network, if the gate acts on more than one site, this will produce an ever larger tensor.
- ’swap+split’: Swap sites until they are adjacent, then contract the gate and split the resulting tensor, then swap the sites back to their original position. In this way an MPS structure can be explicitly maintained at the cost of rising bond-dimension.
tags (str or sequence of str, optional) – Tag the new gate tensor with these tags.
propagate_tags ({'sites', 'register', False, True}, optional) –
Add any tags from the sites to the new gate tensor (only matters if contract=False else tags are merged anyway):
- If 'sites', then only propagate tags matching e.g. ‘I{}’ and ignore all others. I.e. just propagate the lightcone.
- If 'register', then only propagate tags matching the sites of where this gate was actually applied. I.e. ignore the lightcone, just keep track of which ‘registers’ the gate was applied to.
- If False, propagate nothing.
- If True, propagate all tags.
inplace – Perform the gate in place.
bool – Perform the gate in place.
optional – Perform the gate in place.
compress_opts – Supplied to split() if contract='swap+split' or gate_with_auto_swap() if contract='swap+split'.

Return type:

TensorNetwork1DVector

See also

Examples

>>> p = MPS_rand_state(3, 7)
>>> p.gate_(spin_operator('X'), where=1, tags=['GX'])
>>> p
<MatrixProductState(tensors=4, L=3, max_bond=7)>

>>> p.outer_inds()
('k0', 'k1', 'k2')

quimb.tensor.superop_TN_1D(tn_super, tn_op, upper_ind_id='k{}', lower_ind_id='b{}', so_outer_upper_ind_id=None, so_inner_upper_ind_id=None, so_inner_lower_ind_id=None, so_outer_lower_ind_id=None)[source]¶

Take a tensor network superoperator and act with it on a tensor network operator, maintaining the original upper and lower indices of the operator:

outer_upper_ind_id                           upper_ind_id
   | | | ... |                               | | | ... |
   +----------+                              +----------+
   | tn_super +---+                          | tn_super +---+
   +----------+   |     upper_ind_id         +----------+   |
   | | | ... |    |      | | | ... |         | | | ... |    |
inner_upper_ind_id|     +-----------+       +-----------+   |
                  |  +  |   tn_op   |   =   |   tn_op   |   |
inner_lower_ind_id|     +-----------+       +-----------+   |
   | | | ... |    |      | | | ... |         | | | ... |    |
   +----------+   |      lower_ind_id        +----------+   |
   | tn_super +---+                          | tn_super +---+
   +----------+                              +----------+
   | | | ... | <--                           | | | ... |
outer_lower_ind_id                           lower_ind_id

Parameters:

tn_super (TensorNetwork) – The superoperator in the form of a 1D-like tensor network.
tn_op (TensorNetwork) – The operator to be acted on in the form of a 1D-like tensor network.
upper_ind_id (str, optional) – Current id of the upper operator indices, e.g. usually 'k{}'.
lower_ind_id (str, optional) – Current id of the lower operator indices, e.g. usually 'b{}'.
so_outer_upper_ind_id (str, optional) – Current id of the superoperator’s upper outer indices, these will be reindexed to form the new effective operators upper indices.
so_inner_upper_ind_id (str, optional) – Current id of the superoperator’s upper inner indices, these will be joined with those described by upper_ind_id.
so_inner_lower_ind_id (str, optional) – Current id of the superoperator’s lower inner indices, these will be joined with those described by lower_ind_id.
so_outer_lower_ind_id (str, optional) – Current id of the superoperator’s lower outer indices, these will be reindexed to form the new effective operators lower indices.

Returns:

KAK – The tensornetwork of the superoperator acting on the operator.

Return type:

quimb.tensor.enforce_1d_like(tn, site_tags=None, fix_bonds=True, inplace=False)[source]¶

Check that tn is 1D-like with OBC, i.e. 1) that each tensor has exactly one of the given site_tags. If not, raise a ValueError. 2) That there are no hyper indices. And 3) that there are only bonds within sites or between nearest neighbor sites. This issue can be optionally automatically fixed by inserting a string of identity tensors.

Parameters:

tn (TensorNetwork) – The tensor network to check.
site_tags (sequence of str, optional) – The tags to use to group and order the tensors from tn. If not given, uses tn.site_tags.
fix_bonds (bool, optional) – Whether to fix the bond structure by inserting identity tensors.
inplace (bool, optional) – Whether to perform the fix inplace or not.

Raises:

ValueError – If the tensor network is not 1D-like.

quimb.tensor.tensor_network_1d_compress(tn, max_bond=None, cutoff=1e-10, method='dm', site_tags=None, canonize=True, permute_arrays=True, optimize='auto-hq', sweep_reverse=False, equalize_norms=False, compress_opts=None, inplace=False, **kwargs)[source]¶

Compress a 1D-like tensor network using the specified method.

Parameters:

tn (TensorNetwork) – The tensor network to compress. Every tensor should have exactly one of the site tags. Each site can have multiple tensors and output indices.
max_bond (int) – The maximum bond dimension to compress to.
cutoff (float, optional) – A dynamic threshold for discarding singular values when compressing.
method ({"direct", "dm", "zipup", "zipup-first", "fit", "projector", ...}) – The compression method to use.
site_tags (sequence of str, optional) – The tags to use to group and order the tensors from tn. If not given, uses tn.site_tags. The tensor network built will have one tensor per site, in the order given by site_tags.
canonize (bool, optional) – Whether to perform canonicalization, pseudo or otherwise depending on the method, before compressing. Ignored for method='dm' and method='fit'.
permute_arrays (bool or str, optional) – Whether to permute the array indices of the final tensor network into canonical order. If True will use the default order, otherwise if a string this specifies a custom order.
optimize (str, optional) – The contraction path optimizer to use.
sweep_reverse (bool, optional) – Whether to sweep in the reverse direction, resulting in a left canonical form instead of right canonical (for the fit method, this also depends on the last sweep direction).
equalize_norms (bool or float, optional) – Whether to equalize the norms of the tensors after compression. If an explicit value is give, then the norms will be set to that value, and the overall scaling factor will be accumulated into .exponent.
inplace (bool, optional) – Whether to perform the compression inplace.
kwargs – Supplied to the chosen compression method.

Return type:

quimb.tensor.NNI[source]¶

class quimb.tensor.TEBD(p0, H, dt=None, tol=None, t0=0.0, split_opts=None, progbar=True, imag=False)[source]¶

Class implementing Time Evolving Block Decimation (TEBD) [1].

[1] Guifré Vidal, Efficient Classical Simulation of Slightly Entangled Quantum Computations, PRL 91, 147902 (2003)

Parameters:

p0 (MatrixProductState) – Initial state.
H (LocalHam1D or array_like) – Dense hamiltonian representing the two body interaction. Should have shape (d * d, d * d), where d is the physical dimension of p0.
dt (float, optional) – Default time step, cannot be set as well as tol.
tol (float, optional) – Default target error for each evolution, cannot be set as well as dt, which will instead be calculated from the trotter orderm length of time, and hamiltonian norm.
t0 (float, optional) – Initial time. Defaults to 0.0.
split_opts (dict, optional) – Compression options applied for splitting after gate application, see tensor_split().
imag (bool, optional) – Enable imaginary time evolution. Defaults to false.

See also

quimb.Evolution

_pt¶

L¶

H¶

cyclic¶

_ham_norm¶

_err = 0.0¶

tol = None¶

imag = False¶

progbar = True¶

split_opts¶

property pt¶: The MPS state of the system at the current time.

property err¶

choose_time_step(tol, T, order)[source]¶: Trotter error is ~ (T / dt) * dt^(order + 1). Invert to find desired time step, and scale by norm of interaction term.

_get_gate_from_ham(dt_frac, sites)[source]¶: Get the unitary (exponentiated) gate for fraction of timestep dt_frac and sites sites, cached.

sweep(direction, dt_frac, dt=None, queue=False)[source]¶

Perform a single sweep of gates and compression. This shifts the orthonognality centre along with the gates as they are applied and split.

Parameters:

direction ({'right', 'left'}) – Which direction to sweep. Right is even bonds, left is odd.
dt_frac (float) – What fraction of dt substep to take.
dt (float, optional) – Overide the current dt with a custom value.

_step_order2(tau=1, **sweep_opts)[source]¶: Perform a single, second order step.

_step_order4(**sweep_opts)[source]¶: Perform a single, fourth order step.

step(order=2, dt=None, progbar=None, **sweep_opts)[source]¶: Perform a single step of time self.dt.

_compute_sweep_dt_tol(T, dt, tol, order)[source]¶

TARGET_TOL = 1e-13¶

update_to(T, dt=None, tol=None, order=4, progbar=None)[source]¶

Update the state to time T.

Parameters:

T (float) – The time to evolve to.
dt (float, optional) – Time step to use. Can’t be set as well as tol.
tol (float, optional) – Tolerance for whole evolution. Can’t be set as well as dt.
order (int, optional) – Trotter order to use.
progbar (bool, optional) – Manually turn the progress bar off.

_set_progbar_desc(progbar)[source]¶

at_times(ts, dt=None, tol=None, order=4, progbar=None)[source]¶

Generate the time evolved state at each time in ts.

Parameters:

ts (sequence of float) – The times to evolve to and yield the state at.
dt (float, optional) – Time step to use. Can’t be set as well as tol.
tol (float, optional) – Tolerance for whole evolution. Can’t be set as well as dt.
order (int, optional) – Trotter order to use.
progbar (bool, optional) – Manually turn the progress bar off.

Yields:

pt (MatrixProductState) – The state at each of the times in ts. This is a copy of internal state used, so inplace changes can be made to it.

class quimb.tensor.LocalHam1D(L, H2, H1=None, cyclic=False)[source]¶

Bases: quimb.tensor.tensor_arbgeom_tebd.LocalHamGen

An simple interacting hamiltonian object used, for instance, in TEBD. Once instantiated, the LocalHam1D hamiltonian stores a single term per pair of sites, cached versions of which can be retrieved like H.get_gate_expm((i, i + 1), -1j * 0.5) etc.

Parameters:

L (int) – The size of the hamiltonian.
H2 (array_like or dict[tuple[int], array_like]) – The sum of interaction terms. If a dict is given, the keys should be nearest neighbours like (10, 11), apart from any default term which should have the key None, and the values should be the sum of interaction terms for that interaction.
H1 (array_like or dict[int, array_like], optional) – The sum of single site terms. If a dict is given, the keys should be integer sites, apart from any default term which should have the key None, and the values should be the sum of single site terms for that site.
cyclic (bool, optional) – Whether the hamiltonian has periodic boundary conditions or not.

terms¶

The terms in the hamiltonian, combined from the inputs such that there is a single term per pair.

Type:: dict[tuple[int], array]

Examples

A simple, translationally invariant, interaction-only LocalHam1D:

>>> XX = pauli('X') & pauli('X')
>>> YY = pauli('Y') & pauli('Y')
>>> ham = LocalHam1D(L=100, H2=XX + YY)

The same, but with a translationally invariant field as well:

>>> Z = pauli('Z')
>>> ham = LocalHam1D(L=100, H2=XX + YY, H1=Z)

Specifying a default interaction and field, with custom values set for some sites:

>>> H2 = {None: XX + YY, (49, 50): (XX + YY) / 2}
>>> H1 = {None: Z, 49: 2 * Z, 50: 2 * Z}
>>> ham = LocalHam1D(L=100, H2=H2, H1=H1)

Specifying the hamiltonian entirely through site specific interactions and fields:

>>> H2 = {(i, i + 1): XX + YY for i in range(99)}
>>> H1 = {i: Z for i in range(100)}
>>> ham = LocalHam1D(L=100, H2=H2, H1=H1)

See also

SpinHam1D

L¶

cyclic = False¶

mean_norm()[source]¶: Computes the average frobenius norm of local terms.

build_mpo_propagator_trotterized(x, site_tag_id='I{}', tags=None, upper_ind_id='k{}', lower_ind_id='b{}', shape='lrud', contract_sites=True, **split_opts)[source]¶

Build an MPO representation of expm(H * x), i.e. the imaginary or real time propagator of this local 1D hamiltonian, using a first order trotterized decomposition.

Parameters:

x (float) – The time to evolve for. Note this does not include the imaginary prefactor of the Schrodinger equation, so real x corresponds to imaginary time evolution, and vice versa.
site_tag_id (str) – A string specifiying how to tag the tensors at each site. Should contain a '{}' placeholder. It is used to generate the actual tags like: map(site_tag_id.format, range(len(arrays))).
tags (str or sequence of str, optional) – Global tags to attach to all tensors.
upper_ind_id (str) – A string specifiying how to label the upper physical site indices. Should contain a '{}' placeholder. It is used to generate the actual indices like: map(upper_ind_id.format, range(len(arrays))).
lower_ind_id (str) – A string specifiying how to label the lower physical site indices. Should contain a '{}' placeholder. It is used to generate the actual indices like: map(lower_ind_id.format, range(len(arrays))).
shape (str, optional) – String specifying layout of the tensors. E.g. ‘lrud’ (the default) indicates the shape corresponds left-bond, right-bond, ‘up’ physical index, ‘down’ physical index. End tensors have either ‘l’ or ‘r’ dropped from the string if not periodic.
contract_sites (bool, optional) – Whether to contract all the decomposed factors at each site to yield a single tensor per site, by default True.
split_opts – Supplied to tensor_split().

__repr__()[source]¶

class quimb.tensor.PEPO(arrays, *, shape='urdlbk', tags=None, upper_ind_id='k{},{}', lower_ind_id='b{},{}', site_tag_id='I{},{}', x_tag_id='X{}', y_tag_id='Y{}', **tn_opts)[source]¶

Bases: TensorNetwork2DOperator, TensorNetwork2DFlat

Projected Entangled Pair Operator object:

             ...
 │╱   │╱   │╱   │╱   │╱   │╱
 ●────●────●────●────●────●──
╱│   ╱│   ╱│   ╱│   ╱│   ╱│
 │╱   │╱   │╱   │╱   │╱   │╱
 ●────●────●────●────●────●──
╱│   ╱│   ╱│   ╱│   ╱│   ╱│
 │╱   │╱   │╱   │╱   │╱   │╱   ...
 ●────●────●────●────●────●──
╱│   ╱│   ╱│   ╱│   ╱│   ╱│
 │╱   │╱   │╱   │╱   │╱   │╱
 ●────●────●────●────●────●──
╱    ╱    ╱    ╱    ╱    ╱

Parameters:

arrays (sequence of sequence of array) – The core tensor data arrays.
shape (str, optional) – Which order the dimensions of the arrays are stored in, the default 'urdlbk' stands for (‘up’, ‘right’, ‘down’, ‘left’, ‘bra’, ‘ket’). Arrays on the edge of lattice are assumed to be missing the corresponding dimension.
tags (set[str], optional) – Extra global tags to add to the tensor network.
upper_ind_id (str, optional) – String specifier for naming convention of upper site indices.
lower_ind_id (str, optional) – String specifier for naming convention of lower site indices.
site_tag_id (str, optional) – String specifier for naming convention of site tags.
x_tag_id (str, optional) – String specifier for naming convention of row (‘x’) tags.
y_tag_id (str, optional) – String specifier for naming convention of column (‘y’) tags.

_EXTRA_PROPS = ('_site_tag_id', '_x_tag_id', '_y_tag_id', '_Lx', '_Ly', '_upper_ind_id', '_lower_ind_id')¶

_upper_ind_id = 'k{},{}'¶

_lower_ind_id = 'b{},{}'¶

_site_tag_id = 'I{},{}'¶

_x_tag_id = 'X{}'¶

_y_tag_id = 'Y{}'¶

_Lx¶

_Ly¶

classmethod from_fill_fn(fill_fn, Lx, Ly, bond_dim, phys_dim=2, cyclic=False, shape='urdlbk', **pepo_opts)[source]¶

Create a PEPO and fill the tensor entries with a supplied function matching signature fill_fn(shape) -> array.

Parameters:

fill_fn (callable) – A function that takes a shape tuple and returns a data array.
Lx (int) – The number of rows.
Ly (int) – The number of columns.
bond_dim (int) – The bond dimension.
phys_dim (int, optional) – The physical indices dimension.
cyclic (bool or tuple[bool, bool], optional) – Whether the lattice is cyclic in the x and y directions.
shape (str, optional) – How to layout the indices of the tensors, the default is (up, right, down, left bra, ket) == 'urdlbk'.
pepo_opts – Supplied to PEPO.

classmethod rand(Lx, Ly, bond_dim, phys_dim=2, herm=False, dist='normal', loc=0.0, scale=1.0, dtype='float64', seed=None, **pepo_opts)[source]¶

Create a random PEPO.

Parameters:

Lx (int) – The number of rows.
Ly (int) – The number of columns.
bond_dim (int) – The bond dimension.
physical (int, optional) – The physical index dimension.
herm (bool, optional) – Whether to symmetrize the tensors across the physical bonds to make the overall operator hermitian.
dtype (dtype, optional) – The dtype to create the arrays with, default is real double.
seed (int, optional) – A random seed.
pepo_opts – Supplied to PEPO.

Returns:

Return type:

PEPO

rand_herm¶

classmethod zeros(Lx, Ly, bond_dim, phys_dim=2, dtype='float64', backend='numpy', **pepo_opts)[source]¶

Create a PEPO with all zero entries.

Parameters:

Lx (int) – The number of rows.
Ly (int) – The number of columns.
bond_dim (int) – The bond dimension.
physical (int, optional) – The physical index dimension.
dtype (dtype, optional) – The dtype to create the arrays with, default is real double.
backend (str, optional) – Which backend to use, default is 'numpy'.
pepo_opts – Supplied to PEPO.

add_PEPO(other, inplace=False)[source]¶

add_PEPO_[source]¶

_apply_peps(other, compress=False, contract=True, **compress_opts)[source]¶

apply(other, compress=False, **compress_opts)[source]¶

Act with this PEPO on other, returning a new TN like other with the same outer indices.

Parameters:

other (PEPS) – The TN to act on.
compress (bool, optional) – Whether to compress the resulting TN.
compress_opts – Supplied to compress().

Return type:

TensorNetwork2DFlat

show()[source]¶: Print a unicode schematic of this PEPO and its bond dimensions.

class quimb.tensor.PEPS(arrays, *, shape='urdlp', tags=None, site_ind_id='k{},{}', site_tag_id='I{},{}', x_tag_id='X{}', y_tag_id='Y{}', **tn_opts)[source]¶

Bases: TensorNetwork2DVector, TensorNetwork2DFlat

Projected Entangled Pair States object (2D):

             ...
 │    │    │    │    │    │
 ●────●────●────●────●────●──
╱│   ╱│   ╱│   ╱│   ╱│   ╱│
 │    │    │    │    │    │
 ●────●────●────●────●────●──
╱│   ╱│   ╱│   ╱│   ╱│   ╱│
 │    │    │    │    │    │   ...
 ●────●────●────●────●────●──
╱│   ╱│   ╱│   ╱│   ╱│   ╱│
 │    │    │    │    │    │
 ●────●────●────●────●────●──
╱    ╱    ╱    ╱    ╱    ╱

Parameters:

arrays (sequence of sequence of array_like) – The core tensor data arrays.
shape (str, optional) – Which order the dimensions of the arrays are stored in, the default 'urdlp' stands for (‘up’, ‘right’, ‘down’, ‘left’, ‘physical’). Arrays on the edge of lattice are assumed to be missing the corresponding dimension.
tags (set[str], optional) – Extra global tags to add to the tensor network.
site_ind_id (str, optional) – String specifier for naming convention of site indices.
site_tag_id (str, optional) – String specifier for naming convention of site tags.
x_tag_id (str, optional) – String specifier for naming convention of row (‘x’) tags.
y_tag_id (str, optional) – String specifier for naming convention of column (‘y’) tags.

_EXTRA_PROPS = ('_site_tag_id', '_x_tag_id', '_y_tag_id', '_Lx', '_Ly', '_site_ind_id')¶

_site_ind_id = 'k{},{}'¶

_site_tag_id = 'I{},{}'¶

_x_tag_id = 'X{}'¶

_y_tag_id = 'Y{}'¶

_Lx¶

_Ly¶

classmethod from_fill_fn(fill_fn, Lx, Ly, bond_dim, phys_dim=2, cyclic=False, shape='urdlp', **peps_opts)[source]¶

Create a 2D PEPS from a filling function with signature fill_fn(shape).

Parameters:

Lx (int) – The number of rows.
Ly (int) – The number of columns.
bond_dim (int) – The bond dimension.
phys_dim (int, optional) – The physical index dimension.
cyclic (bool or tuple[bool, bool], optional) – Whether the lattice is cyclic in the x and y directions.
shape (str, optional) – How to layout the indices of the tensors, the default is (up, right, down, left, phys) == 'urdlbk'. This is the order of the shape supplied to the filling function.
peps_opts – Supplied to PEPS.

Returns:

psi

Return type:

classmethod empty(Lx, Ly, bond_dim, phys_dim=2, like='numpy', **peps_opts)[source]¶

Create an empty 2D PEPS.

Parameters:

Lx (int) – The number of rows.
Ly (int) – The number of columns.
bond_dim (int) – The bond dimension.
physical (int, optional) – The physical index dimension.
peps_opts – Supplied to PEPS.

Returns:

psi

Return type:

See also

classmethod ones(Lx, Ly, bond_dim, phys_dim=2, like='numpy', **peps_opts)[source]¶

Create a 2D PEPS whose tensors are filled with ones.

Parameters:

Lx (int) – The number of rows.
Ly (int) – The number of columns.
bond_dim (int) – The bond dimension.
physical (int, optional) – The physical index dimension.
peps_opts – Supplied to PEPS.

Returns:

psi

Return type:

See also

classmethod zeros(Lx, Ly, bond_dim, phys_dim=2, like='numpy', **peps_opts)[source]¶

Create a 2D PEPS whose tensors are filled with zeros.

Parameters:

Lx (int) – The number of rows.
Ly (int) – The number of columns.
bond_dim (int) – The bond dimension.
physical (int, optional) – The physical index dimension.
peps_opts – Supplied to PEPS.

Returns:

psi

Return type:

See also

classmethod rand(Lx, Ly, bond_dim, phys_dim=2, dist='normal', loc=0.0, scale=1.0, dtype='float64', seed=None, **peps_opts)[source]¶

Create a random (un-normalized) PEPS.

Parameters:

Lx (int) – The number of rows.
Ly (int) – The number of columns.
bond_dim (int) – The bond dimension.
physical (int, optional) – The physical index dimension.
dist ({'normal', 'uniform', 'rademacher', 'exp'}, optional) – Type of random number to generate, defaults to ‘normal’.
loc (float, optional) – An additive offset to add to the random numbers.
dtype (dtype, optional) – The dtype to create the arrays with, default is real double.
seed (int, optional) – A random seed.
peps_opts – Supplied to PEPS.

Returns:

psi

Return type:

See also

classmethod product_state(site_map, cyclic=False, **peps_opts)[source]¶

Create a PEPS representing a product state, with explicit bonds of dimension 1 between sites.

Parameters:

site_map (dict[tuple[int, int], array] or Sequence[Sequence[array]]) – A mapping of site coordinates to physical vectors, or a 2D array of physical vectors. Each vector being a single site state.
cyclic (bool or tuple[bool, bool], optional) – Whether the lattice is cyclic in the x and y directions.
peps_opts – Supplied to PEPS.

Return type:

classmethod vacuum(Lx, Ly, phys_dim=2, **peps_opts)[source]¶

Create the ‘vaccum’ state PEPS, i.e. |00…0>.

Parameters:

Lx (int) – The number of rows.
Ly (int) – The number of columns.
phys_dim (int, optional) – The physical index dimension.
peps_opts – Supplied to product_state.

add_PEPS(other, inplace=False)[source]¶

add_PEPS_[source]¶

show()[source]¶: Print a unicode schematic of this PEPS and its bond dimensions.

class quimb.tensor.TensorNetwork2D(ts=(), *, virtual=False, check_collisions=True)[source]¶

Bases: quimb.tensor.tensor_arbgeom.TensorNetworkGen

Mixin class for tensor networks with a square lattice two-dimensional structure, indexed by [{row},{column}] so that:

             'Y{j}'
                v

i=Lx-1 ●──●──●──●──●──●──   ──●
       |  |  |  |  |  |       |
             ...
       |  |  |  |  |  | 'I{i},{j}' = 'I3,5' e.g.
i=3    ●──●──●──●──●──●──
       |  |  |  |  |  |       |
i=2    ●──●──●──●──●──●──   ──●    <== 'X{i}'
       |  |  |  |  |  |  ...  |
i=1    ●──●──●──●──●──●──   ──●
       |  |  |  |  |  |       |
i=0    ●──●──●──●──●──●──   ──●

     j=0, 1, 2, 3, 4, 5    j=Ly-1

This implies the following conventions:

the ‘up’ bond is coordinates (i, j), (i + 1, j)

the ‘down’ bond is coordinates (i, j), (i - 1, j)

the ‘right’ bond is coordinates (i, j), (i, j + 1)

the ‘left’ bond is coordinates (i, j), (i, j - 1)

_NDIMS = 2¶

_EXTRA_PROPS = ('_site_tag_id', '_x_tag_id', '_y_tag_id', '_Lx', '_Ly')¶

_compatible_2d(other)[source]¶: Check whether self and other are compatible 2D tensor networks such that they can remain a 2D tensor network when combined.

combine(other, *, virtual=False, check_collisions=True)[source]¶

Combine this tensor network with another, returning a new tensor network. If the two are compatible, cast the resulting tensor network to a TensorNetwork2D instance.

Parameters:

other (TensorNetwork2D or TensorNetwork) – The other tensor network to combine with.
virtual (bool, optional) – Whether the new tensor network should copy all the incoming tensors (False, the default), or view them as virtual (True).
check_collisions (bool, optional) – Whether to check for index collisions between the two tensor networks before combining them. If True (the default), any inner indices that clash will be mangled.

Return type:

TensorNetwork2D or TensorNetwork

property Lx¶: The number of rows.

property Ly¶: The number of columns.

property nsites¶: The total number of sites.

site_tag(i, j=None)[source]¶: The name of the tag specifiying the tensor at site (i, j).

property x_tag_id¶: The string specifier for tagging each row of this 2D TN.

x_tag(i)[source]¶

property x_tags¶: A tuple of all of the Lx different row tags.

row_tag[source]¶

row_tags¶

property y_tag_id¶: The string specifier for tagging each column of this 2D TN.

y_tag(j)[source]¶

property y_tags¶: A tuple of all of the Ly different column tags.

col_tag[source]¶

col_tags¶

maybe_convert_coo(x)[source]¶: Check if x is a tuple of two ints and convert to the corresponding site tag if so.

_get_tids_from_tags(tags, which='all')[source]¶: This is the function that lets coordinates such as (i, j) be used for many ‘tag’ based functions.

gen_site_coos()[source]¶: Generate coordinates for all the sites in this 2D TN.

gen_bond_coos()[source]¶: Generate pairs of coordinates for all the bonds in this 2D TN.

gen_horizontal_bond_coos()[source]¶: Generate all coordinate pairs like (i, j), (i, j + 1).

gen_horizontal_even_bond_coos()[source]¶: Generate all coordinate pairs like (i, j), (i, j + 1) where j is even, which thus don’t overlap at all.

gen_horizontal_odd_bond_coos()[source]¶: Generate all coordinate pairs like (i, j), (i, j + 1) where j is odd, which thus don’t overlap at all.

gen_vertical_bond_coos()[source]¶: Generate all coordinate pairs like (i, j), (i + 1, j).

gen_vertical_even_bond_coos()[source]¶: Generate all coordinate pairs like (i, j), (i + 1, j) where i is even, which thus don’t overlap at all.

gen_vertical_odd_bond_coos()[source]¶: Generate all coordinate pairs like (i, j), (i + 1, j) where i is odd, which thus don’t overlap at all.

gen_diagonal_left_bond_coos()[source]¶: Generate all coordinate pairs like (i, j), (i + 1, j - 1).

gen_diagonal_left_even_bond_coos()[source]¶: Generate all coordinate pairs like (i, j), (i + 1, j - 1) where j is even, which thus don’t overlap at all.

gen_diagonal_left_odd_bond_coos()[source]¶: Generate all coordinate pairs like (i, j), (i + 1, j - 1) where j is odd, which thus don’t overlap at all.

gen_diagonal_right_bond_coos()[source]¶: Generate all coordinate pairs like (i, j), (i + 1, j + 1).

gen_diagonal_right_even_bond_coos()[source]¶: Generate all coordinate pairs like (i, j), (i + 1, j + 1) where i is even, which thus don’t overlap at all.

gen_diagonal_right_odd_bond_coos()[source]¶: Generate all coordinate pairs like (i, j), (i + 1, j + 1) where i is odd, which thus don’t overlap at all.

gen_diagonal_bond_coos()[source]¶: Generate all next nearest neighbor diagonal coordinate pairs.

valid_coo(coo, xrange=None, yrange=None)[source]¶

Check whether coo is in-bounds.

Parameters:

coo ((int, int, int), optional) – The coordinates to check.
xrange ((int, int), optional) – The range of allowed values for the x and y coordinates.
yrange ((int, int), optional) – The range of allowed values for the x and y coordinates.

Return type:

bool

get_ranges_present()[source]¶

Return the range of site coordinates present in this TN.

Returns:: xrange, yrange – The minimum and maximum site coordinates present in each direction.
Return type:: tuple[tuple[int, int]]

is_cyclic_x(j=None, imin=None, imax=None)[source]¶: Check if the x dimension is cyclic (periodic), specifically whether a bond exists between (imin, j) and (imax, j), with default values of imin = 0 and imax = Lx - 1, and j at the center of the lattice. If imin and imax are adjacent then this is considered False, since there is no ‘extra’ connectivity.

is_cyclic_y(i=None, jmin=None, jmax=None)[source]¶: Check if the y dimension is cyclic (periodic), specifically whether a bond exists between (i, jmin) and (i, jmax), with default values of jmin = 0 and jmax = Ly - 1, and i at the center of the lattice. If jmin and jmax are adjacent then this is considered False, since there is no ‘extra’ connectivity.

__getitem__(key)[source]¶: Key based tensor selection, checking for integer based shortcut.

show()[source]¶: Print a unicode schematic of this 2D TN and its bond dimensions.

_repr_info()[source]¶: General info to show in various reprs. Sublasses can add more relevant info to this dict.

flatten(fuse_multibonds=True, inplace=False)[source]¶: Contract all tensors corresponding to each site into one.

flatten_[source]¶

gen_pairs(xrange=None, yrange=None, xreverse=False, yreverse=False, coordinate_order='xy', xstep=None, ystep=None, stepping_order='xy', step_only=None)[source]¶

Helper function for generating pairs of cooordinates for all bonds within a certain range, optionally specifying an order.

Parameters:

xrange ((int, int), optional) – The range of allowed values for the x and y coordinates.
yrange ((int, int), optional) – The range of allowed values for the x and y coordinates.
xreverse (bool, optional) – Whether to reverse the order of the x and y sweeps.
yreverse (bool, optional) – Whether to reverse the order of the x and y sweeps.
coordinate_order (str, optional) – The order in which to sweep the x and y coordinates. Earlier dimensions will change slower. If the corresponding range has size 1 then that dimension doesn’t need to be specified.
xstep (int, optional) – When generating a bond, step in this direction to yield the neighboring coordinate. By default, these follow xreverse and yreverse respectively.
ystep (int, optional) – When generating a bond, step in this direction to yield the neighboring coordinate. By default, these follow xreverse and yreverse respectively.
stepping_order (str, optional) – The order in which to step the x and y coordinates to generate bonds. Does not need to include all dimensions.
step_only (int, optional) – Only perform the ith steps in stepping_order, used to interleave canonizing and compressing for example.

Yields:

coo_a, coo_b (((int, int), (int, int)))

canonize_plane(xrange, yrange, equalize_norms=False, canonize_opts=None, **gen_pair_opts)[source]¶: Canonize every pair of tensors within a subrange, optionally specifying a order to visit those pairs in.

canonize_row(i, sweep, yrange=None, **canonize_opts)[source]¶

Canonize all or part of a row.

If sweep == 'right' then:

 |  |  |  |  |  |  |         |  |  |  |  |  |  |
─●──●──●──●──●──●──●─       ─●──●──●──●──●──●──●─
 |  |  |  |  |  |  |         |  |  |  |  |  |  |
─●──●──●──●──●──●──●─  ==>  ─●──>──>──>──>──o──●─ row=i
 |  |  |  |  |  |  |         |  |  |  |  |  |  |
─●──●──●──●──●──●──●─       ─●──●──●──●──●──●──●─
 |  |  |  |  |  |  |         |  |  |  |  |  |  |
    .           .               .           .
    jstart      jstop           jstart      jstop

If sweep == 'left' then:

 |  |  |  |  |  |  |         |  |  |  |  |  |  |
─●──●──●──●──●──●──●─       ─●──●──●──●──●──●──●─
 |  |  |  |  |  |  |         |  |  |  |  |  |  |
─●──●──●──●──●──●──●─  ==>  ─●──o──<──<──<──<──●─ row=i
 |  |  |  |  |  |  |         |  |  |  |  |  |  |
─●──●──●──●──●──●──●─       ─●──●──●──●──●──●──●─
 |  |  |  |  |  |  |         |  |  |  |  |  |  |
    .           .               .           .
    jstop       jstart          jstop       jstart

Does not yield an orthogonal form in the same way as in 1D.

Parameters:

i (int) – Which row to canonize.
sweep ({'right', 'left'}) – Which direction to sweep in.
jstart (int or None) – Starting column, defaults to whole row.
jstop (int or None) – Stopping column, defaults to whole row.
canonize_opts – Supplied to canonize_between.

canonize_column(j, sweep, xrange=None, **canonize_opts)[source]¶

Canonize all or part of a column.

If sweep='up' then:

 |  |  |         |  |  |
─●──●──●─       ─●──●──●─
 |  |  |         |  |  |
─●──●──●─       ─●──o──●─ istop
 |  |  |   ==>   |  |  |
─●──●──●─       ─●──^──●─
 |  |  |         |  |  |
─●──●──●─       ─●──^──●─ istart
 |  |  |         |  |  |
─●──●──●─       ─●──●──●─
 |  |  |         |  |  |
    .               .
    j               j

If sweep='down' then:

 |  |  |         |  |  |
─●──●──●─       ─●──●──●─
 |  |  |         |  |  |
─●──●──●─       ─●──v──●─ istart
 |  |  |   ==>   |  |  |
─●──●──●─       ─●──v──●─
 |  |  |         |  |  |
─●──●──●─       ─●──o──●─ istop
 |  |  |         |  |  |
─●──●──●─       ─●──●──●─
 |  |  |         |  |  |
    .               .
    j               j

Does not yield an orthogonal form in the same way as in 1D.

Parameters:

j (int) – Which column to canonize.
sweep ({'up', 'down'}) – Which direction to sweep in.
xrange (None or (int, int), optional) – The range of columns to canonize.
canonize_opts – Supplied to canonize_between.

canonize_row_around(i, around=(0, 1))[source]¶

compress_plane(xrange, yrange, max_bond=None, cutoff=1e-10, equalize_norms=False, compress_opts=None, **gen_pair_opts)[source]¶: Compress every pair of tensors within a subrange, optionally specifying a order to visit those pairs in.

compress_row(i, sweep, yrange=None, max_bond=None, cutoff=1e-10, equalize_norms=False, compress_opts=None)[source]¶

Compress all or part of a row.

If sweep == 'right' then:

 |  |  |  |  |  |  |         |  |  |  |  |  |  |
━●━━●━━●━━●━━●━━●━━●━       ━●━━●━━●━━●━━●━━●━━●━
 |  |  |  |  |  |  |         |  |  |  |  |  |  |
━●━━●━━●━━●━━●━━●━━●━  ━━>  ━●━━>──>──>──>──o━━●━ row=i
 |  |  |  |  |  |  |         |  |  |  |  |  |  |
━●━━●━━●━━●━━●━━●━━●━       ━●━━●━━●━━●━━●━━●━━●━
 |  |  |  |  |  |  |         |  |  |  |  |  |  |
    .           .               .           .
    jstart      jstop           jstart      jstop

If sweep == 'left' then:

 |  |  |  |  |  |  |         |  |  |  |  |  |  |
━●━━●━━●━━●━━●━━●━━●━       ━●━━●━━●━━●━━●━━●━━●━
 |  |  |  |  |  |  |         |  |  |  |  |  |  |
━●━━●━━●━━●━━●━━●━━●━  ━━>  ━●━━o──<──<──<──<━━●━ row=i
 |  |  |  |  |  |  |         |  |  |  |  |  |  |
━●━━●━━●━━●━━●━━●━━●━       ━●━━●━━●━━●━━●━━●━━●━
 |  |  |  |  |  |  |         |  |  |  |  |  |  |
    .           .               .           .
    jstop       jstart          jstop       jstart

Does not yield an orthogonal form in the same way as in 1D.

Parameters:

i (int) – Which row to compress.
sweep ({'right', 'left'}) – Which direction to sweep in.
yrange (tuple[int, int] or None) – The range of columns to compress.
max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
compress_opts (None or dict, optional) – Supplied to compress_between().

compress_column(j, sweep, xrange=None, max_bond=None, cutoff=1e-10, equalize_norms=False, compress_opts=None)[source]¶

Compress all or part of a column.

If sweep='up' then:

 ┃  ┃  ┃         ┃  ┃  ┃
─●──●──●─       ─●──●──●─
 ┃  ┃  ┃         ┃  ┃  ┃
─●──●──●─       ─●──o──●─  .
 ┃  ┃  ┃   ==>   ┃  |  ┃   .
─●──●──●─       ─●──^──●─  . xrange
 ┃  ┃  ┃         ┃  |  ┃   .
─●──●──●─       ─●──^──●─  .
 ┃  ┃  ┃         ┃  ┃  ┃
─●──●──●─       ─●──●──●─
 ┃  ┃  ┃         ┃  ┃  ┃
    .               .
    j               j

If sweep='down' then:

 ┃  ┃  ┃         ┃  ┃  ┃
─●──●──●─       ─●──●──●─
 ┃  ┃  ┃         ┃  ┃  ┃
─●──●──●─       ─●──v──●─ .
 ┃  ┃  ┃   ==>   ┃  |  ┃  .
─●──●──●─       ─●──v──●─ . xrange
 ┃  ┃  ┃         ┃  |  ┃  .
─●──●──●─       ─●──o──●─ .
 ┃  ┃  ┃         ┃  ┃  ┃
─●──●──●─       ─●──●──●─
 ┃  ┃  ┃         ┃  ┃  ┃
    .               .
    j               j

Does not yield an orthogonal form in the same way as in 1D.

Parameters:

j (int) – Which column to compress.
sweep ({'up', 'down'}) – Which direction to sweep in.
xrange (None or (int, int), optional) – The range of rows to compress.
max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
compress_opts (None or dict, optional) – Supplied to compress_between().

_contract_boundary_core_via_1d(xrange, yrange, from_which, max_bond, cutoff=1e-10, method='dm', layer_tags=None, **compress_opts)[source]¶

_contract_boundary_core(xrange, yrange, from_which, max_bond, cutoff=1e-10, canonize=True, layer_tags=None, compress_late=True, sweep_reverse=False, equalize_norms=False, compress_opts=None, canonize_opts=None)[source]¶

_contract_boundary_full_bond(xrange, yrange, from_which, max_bond, cutoff=0.0, method='eigh', renorm=False, optimize='auto-hq', opposite_envs=None, equalize_norms=False, contract_boundary_opts=None)[source]¶

Contract the boundary of this 2D TN using the ‘full bond’ environment information obtained from a boundary contraction in the opposite direction.

Parameters:

xrange ((int, int) or None, optional) – The range of rows to contract and compress.
yrange ((int, int)) – The range of columns to contract and compress.
from_which ({'xmin', 'ymin', 'xmax', 'ymax'}) – Which direction to contract the rectangular patch from.
max_bond (int) – The maximum boundary dimension, AKA ‘chi’. By default used for the opposite direction environment contraction as well.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction - only for the opposite direction environment contraction.
method ({'eigh', 'eig', 'svd', 'biorthog'}, optional) – Which similarity decomposition method to use to compress the full bond environment.
renorm (bool, optional) – Whether to renormalize the isometric projection or not.
optimize (str or PathOptimize, optimize) – Contraction optimizer to use for the exact contractions.
opposite_envs (dict, optional) – If supplied, the opposite environments will be fetched or lazily computed into this dict depending on whether they are missing.
contract_boundary_opts – Other options given to the opposite direction environment contraction.

_contract_boundary_projector(xrange, yrange, from_which, max_bond=None, cutoff=1e-10, lazy=False, equalize_norms=False, optimize='auto-hq', compress_opts=None)[source]¶

Contract the boundary of this 2D tensor network by explicitly computing and inserting explicit local projector tensors, which can optionally be left uncontracted. Multilayer networks are naturally supported.

Parameters:

xrange (tuple) – The range of x indices to contract.
yrange (tuple) – The range of y indices to contract.
from_which ({'xmin', 'xmax', 'ymin', 'ymax'}) – From which boundary to contract.
max_bond (int, optional) – The maximum bond dimension to contract to. If None (default), compression is left to cutoff.
cutoff (float, optional) – The cutoff to use for boundary compression.
lazy (bool, optional) – Whether to leave the boundary tensors uncontracted. If False (the default), the boundary tensors are contracted and the resulting boundary has a single tensor per site.
equalize_norms (bool, optional) – Whether to actively absorb the norm of modified tensors into self.exponent.
optimize (str or PathOptimizer, optional) – The contract path optimization to use when forming the projector tensors.
compress_opts (dict, optional) – Other options to pass to svd_truncated().

contract_boundary_from(xrange, yrange, from_which, max_bond=None, *, cutoff=1e-10, canonize=True, mode='mps', layer_tags=None, sweep_reverse=False, compress_opts=None, inplace=False, **contract_boundary_opts)[source]¶: Unified entrypoint for contracting any rectangular patch of tensors from any direction, with any boundary method.

contract_boundary_from_[source]¶

contract_boundary_from_xmin(xrange, yrange=None, max_bond=None, *, cutoff=1e-10, canonize=True, mode='mps', layer_tags=None, sweep_reverse=False, compress_opts=None, inplace=False, **contract_boundary_opts)[source]¶

Contract a 2D tensor network inwards from the bottom, canonizing and compressing (left to right) along the way. If layer_tags is None this looks like:

a) contract

│  │  │  │  │
●──●──●──●──●       │  │  │  │  │
│  │  │  │  │  -->  ●══●══●══●══●
●──●──●──●──●

b) optionally canonicalize

│  │  │  │  │
●══●══<══<══<

c) compress in opposite direction

│  │  │  │  │  -->  │  │  │  │  │  -->  │  │  │  │  │
>──●══●══●══●  -->  >──>──●══●══●  -->  >──>──>──●══●
.  .           -->     .  .        -->        .  .

If layer_tags is specified, each then each layer is contracted in and compressed separately, resulting generally in a lower memory scaling. For two layer tags this looks like:

a) first flatten the outer boundary only

│ ││ ││ ││ ││ │       │ ││ ││ ││ ││ │
●─○●─○●─○●─○●─○       ●─○●─○●─○●─○●─○
│ ││ ││ ││ ││ │  ==>   ╲│ ╲│ ╲│ ╲│ ╲│
●─○●─○●─○●─○●─○         ●══●══●══●══●

b) contract and compress a single layer only

│ ││ ││ ││ ││ │
│ ○──○──○──○──○
│╱ │╱ │╱ │╱ │╱
●══<══<══<══<

c) contract and compress the next layer

╲│ ╲│ ╲│ ╲│ ╲│
 >══>══>══>══●

Parameters:

xrange ((int, int)) – The range of rows to compress (inclusive).
yrange ((int, int) or None, optional) – The range of columns to compress (inclusive), sweeping along with canonization and compression. Defaults to all columns.
max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
canonize (bool, optional) – Whether to sweep one way with canonization before compressing.
mode ({'mps', 'full-bond'}, optional) – How to perform the compression on the boundary.
layer_tags (None or sequence[str], optional) – If None, all tensors at each coordinate pair [(i, j), (i + 1, j)] will be first contracted. If specified, then the outer tensor at (i, j) will be contracted with the tensor specified by [(i + 1, j), layer_tag], for each layer_tag in layer_tags.
sweep_reverse (bool, optional) – Which way to perform the compression sweep, which has an effect on which tensors end up being canonized. Setting this to true sweeps the compression from largest to smallest coordinates.
compress_opts (None or dict, optional) – Supplied to compress_between().
inplace (bool, optional) – Whether to perform the contraction inplace or not.

contract_boundary_from_xmin_[source]¶

contract_boundary_from_xmax(xrange, yrange=None, max_bond=None, *, cutoff=1e-10, canonize=True, mode='mps', layer_tags=None, inplace=False, sweep_reverse=False, compress_opts=None, **contract_boundary_opts)[source]¶

Contract a 2D tensor network inwards from the top, canonizing and compressing (right to left) along the way. If layer_tags is None this looks like:

a) contract

●──●──●──●──●
|  |  |  |  |  -->  ●══●══●══●══●
●──●──●──●──●       |  |  |  |  |
|  |  |  |  |

b) optionally canonicalize

●══●══<══<══<
|  |  |  |  |

c) compress in opposite direction

>──●══●══●══●  -->  >──>──●══●══●  -->  >──>──>──●══●
|  |  |  |  |  -->  |  |  |  |  |  -->  |  |  |  |  |
.  .           -->     .  .        -->        .  .

If layer_tags is specified, each then each layer is contracted in and compressed separately, resulting generally in a lower memory scaling. For two layer tags this looks like:

a) first flatten the outer boundary only

●─○●─○●─○●─○●─○         ●══●══●══●══●
│ ││ ││ ││ ││ │  ==>   ╱│ ╱│ ╱│ ╱│ ╱│
●─○●─○●─○●─○●─○       ●─○●─○●─○●─○●─○
│ ││ ││ ││ ││ │       │ ││ ││ ││ ││ │

b) contract and compress a single layer only

●══<══<══<══<
│╲ │╲ │╲ │╲ │╲
│ ○──○──○──○──○
│ ││ ││ ││ ││ │

c) contract and compress the next layer

 ●══●══●══●══●
╱│ ╱│ ╱│ ╱│ ╱│

Parameters:

xrange ((int, int)) – The range of rows to compress (inclusive).
yrange ((int, int) or None, optional) – The range of columns to compress (inclusive), sweeping along with canonization and compression. Defaults to all columns.
max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
canonize (bool, optional) – Whether to sweep one way with canonization before compressing.
mode ({'mps', 'full-bond'}, optional) – How to perform the compression on the boundary.
layer_tags (None or str, optional) – If None, all tensors at each coordinate pair [(i, j), (i - 1, j)] will be first contracted. If specified, then the outer tensor at (i, j) will be contracted with the tensor specified by [(i - 1, j), layer_tag], for each layer_tag in layer_tags.
sweep_reverse (bool, optional) – Which way to perform the compression sweep, which has an effect on which tensors end up being canonized. Setting this to true sweeps the compression from largest to smallest coordinates.
compress_opts (None or dict, optional) – Supplied to compress_between().
inplace (bool, optional) – Whether to perform the contraction inplace or not.

contract_boundary_from_xmax_[source]¶

contract_boundary_from_ymin(yrange, xrange=None, max_bond=None, *, cutoff=1e-10, canonize=True, mode='mps', layer_tags=None, sweep_reverse=False, compress_opts=None, inplace=False, **contract_boundary_opts)[source]¶

Contract a 2D tensor network inwards from the left, canonizing and compressing (bottom to top) along the way. If layer_tags is None this looks like:

a) contract

●──●──       ●──
│  │         ║
●──●──  ==>  ●──
│  │         ║
●──●──       ●──

b) optionally canonicalize

●──       v──
║         ║
●──  ==>  v──
║         ║
●──       ●──

c) compress in opposite direction

v──       ●──
║         │
v──  ==>  ^──
║         │
●──       ^──

If layer_tags is specified, each then each layer is contracted in and compressed separately, resulting generally in a lower memory scaling. For two layer tags this looks like:

a) first flatten the outer boundary only

○──○──           ●──○──
│╲ │╲            │╲ │╲
●─○──○──         ╰─●──○──
 ╲│╲╲│╲     ==>    │╲╲│╲
  ●─○──○──         ╰─●──○──
   ╲│ ╲│             │ ╲│
    ●──●──           ╰──●──

b) contract and compress a single layer only

   ○──
 ╱╱ ╲
●─── ○──
 ╲ ╱╱ ╲
  ^─── ○──
   ╲ ╱╱
    ^─────

c) contract and compress the next layer

●──
│╲
╰─●──
  │╲
  ╰─●──
    │
    ╰──

Parameters:

yrange ((int, int)) – The range of columns to compress (inclusive).
xrange ((int, int) or None, optional) – The range of rows to compress (inclusive), sweeping along with canonization and compression. Defaults to all rows.
max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
canonize (bool, optional) – Whether to sweep one way with canonization before compressing.
mode ({'mps', 'full-bond'}, optional) – How to perform the compression on the boundary.
layer_tags (None or str, optional) – If None, all tensors at each coordinate pair [(i, j), (i, j + 1)] will be first contracted. If specified, then the outer tensor at (i, j) will be contracted with the tensor specified by [(i + 1, j), layer_tag], for each layer_tag in layer_tags.
sweep_reverse (bool, optional) – Which way to perform the compression sweep, which has an effect on which tensors end up being canonized. Setting this to true sweeps the compression from largest to smallest coordinates.
compress_opts (None or dict, optional) – Supplied to compress_between().
inplace (bool, optional) – Whether to perform the contraction inplace or not.

contract_boundary_from_ymin_[source]¶

contract_boundary_from_ymax(yrange, xrange=None, max_bond=None, *, cutoff=1e-10, canonize=True, mode='mps', layer_tags=None, sweep_reverse=False, compress_opts=None, inplace=False, **contract_boundary_opts)[source]¶

Contract a 2D tensor network inwards from the left, canonizing and compressing (top to bottom) along the way. If layer_tags is None this looks like:

a) contract

──●──●       ──●
  │  │         ║
──●──●  ==>  ──●
  │  │         ║
──●──●       ──●

b) optionally canonicalize

──●       ──v
  ║         ║
──●  ==>  ──v
  ║         ║
──●       ──●

c) compress in opposite direction

──v       ──●
  ║         │
──v  ==>  ──^
  ║         │
──●       ──^

If layer_tags is specified, each then each layer is contracted in and compressed separately, resulting generally in a lower memory scaling. For two layer tags this looks like:

a) first flatten the outer boundary only

    ──○──○           ──○──●
     ╱│ ╱│            ╱│ ╱│
  ──○──○─●         ──○──●─╯
   ╱│╱╱│╱   ==>     ╱│╱╱│
──○──○─●         ──○──●─╯
  │╱ │╱            │╱ │
──●──●           ──●──╯

b) contract and compress a single layer only

    ──○
     ╱ ╲╲
  ──○────v
   ╱ ╲╲ ╱
──○────v
   ╲╲ ╱
─────●

c) contract and compress the next layer

       ╲
    ────v
     ╲ ╱
  ────v
   ╲ ╱
────●

Parameters:

yrange ((int, int)) – The range of columns to compress (inclusive).
xrange ((int, int) or None, optional) – The range of rows to compress (inclusive), sweeping along with canonization and compression. Defaults to all rows.
max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
canonize (bool, optional) – Whether to sweep one way with canonization before compressing.
mode ({'mps', 'full-bond'}, optional) – How to perform the compression on the boundary.
layer_tags (None or str, optional) – If None, all tensors at each coordinate pair [(i, j), (i, j - 1)] will be first contracted. If specified, then the outer tensor at (i, j) will be contracted with the tensor specified by [(i + 1, j), layer_tag], for each layer_tag in layer_tags.
sweep_reverse (bool, optional) – Which way to perform the compression sweep, which has an effect on which tensors end up being canonized. Setting this to true sweeps the compression from largest to smallest coordinates.
compress_opts (None or dict, optional) – Supplied to compress_between().
inplace (bool, optional) – Whether to perform the contraction inplace or not.

contract_boundary_from_ymax_[source]¶

_contract_interleaved_boundary_sequence(*, contract_boundary_opts=None, sequence=None, xmin=None, xmax=None, ymin=None, ymax=None, max_separation=1, max_unfinished=1, around=None, equalize_norms=False, canonize=False, canonize_opts=None, final_contract=True, final_contract_opts=None, progbar=False, inplace=False)[source]¶: Unified handler for performing iterleaved contractions in a sequence of inwards boundary directions.

contract_boundary(max_bond=None, *, cutoff=1e-10, canonize=True, mode='mps', layer_tags=None, compress_opts=None, sequence=None, xmin=None, xmax=None, ymin=None, ymax=None, max_separation=1, max_unfinished=1, around=None, equalize_norms=False, final_contract=True, final_contract_opts=None, progbar=None, inplace=False, **contract_boundary_opts)[source]¶

Contract the boundary of this 2D tensor network inwards:

●──●──●──●       ●──●──●──●       ●──●──●
│  │  │  │       │  │  │  │       ║  │  │
●──●──●──●       ●──●──●──●       ^──●──●       >══>══●       >──v
│  │ij│  │  ==>  │  │ij│  │  ==>  ║ij│  │  ==>  │ij│  │  ==>  │ij║
●──●──●──●       ●══<══<══<       ^──<──<       ^──<──<       ^──<
│  │  │  │
●──●──●──●

Optionally from any or all of the boundary, in multiple layers, and stopping around a region. The default is to contract the boundary from the two shortest opposing sides.

Parameters:

around (None or sequence of (int, int), optional) – If given, don’t contract the square of sites bounding these coordinates.
max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
canonize (bool, optional) – Whether to sweep one way with canonization before compressing.
mode ({'mps', 'full-bond', ...}, optional) – How to perform the compression on the boundary, can also be any of the generic 1D or arbgeom methods.
layer_tags (None or sequence of str, optional) – If given, perform a multilayer contraction, contracting the inner sites in each layer into the boundary individually.
compress_opts (None or dict, optional) – Other low level options to pass to compress_between().
sequence (sequence of {'xmin', 'xmax', 'ymin', 'ymax'}, optional) – Which directions to cycle throught when performing the inwards contractions, i.e. from that direction. If around is specified you will likely need all of these! Default is to contract from the two shortest opposing sides.
xmin (int, optional) – The initial bottom boundary row, defaults to 0.
xmax (int, optional) – The initial top boundary row, defaults to Lx - 1.
ymin (int, optional) – The initial left boundary column, defaults to 0.
ymax (int, optional) – The initial right boundary column, defaults to Ly - 1..
max_separation (int, optional) – If around is None, when any two sides become this far apart simply contract the remaining tensor network.
max_unfinished (int, optional) – If around is None, when this many sides are still not within max_separation simply contract the remaining tensor network.
around – If given, don’t contract the square of sites bounding these coordinates.
equalize_norms (bool or float, optional) – Whether to equalize the norms of the boundary tensors after each contraction, gathering the overall scaling coefficient, log10, in tn.exponent.
final_contract (bool, optional) – Whether to exactly contract the remaining tensor network after the boundary contraction.
final_contract_opts (None or dict, optional) – Options to pass to contract(), optimize defaults to 'auto-hq'.
progbar (bool, optional) – Whether to show a progress bar.
inplace (bool, optional) – Whether to perform the contraction in place or not.
contract_boundary_opts – Supplied to contract_boundary_from(), including compression and canonization options.

contract_boundary_[source]¶

contract_mps_sweep(max_bond=None, *, cutoff=1e-10, canonize=True, direction=None, **contract_boundary_opts)[source]¶

Contract this 2D tensor network by sweeping an MPS across from one side to the other.

Parameters:

max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
canonize (bool, optional) – Whether to sweep one way with canonization before compressing.
direction ({'xmin', 'xmax', 'ymin', 'ymax'}, optional) – Which direction to sweep from. If None (default) then the shortest boundary is chosen.
contract_boundary_opts – Supplied to contract_boundary_from(), including compression and canonization options.

contract_mps_sweep_[source]¶

contract_full_bootstrap(n, *, optimize='auto-hq', **kwargs)[source]¶

compute_environments(from_which, xrange=None, yrange=None, max_bond=None, *, cutoff=1e-10, canonize=True, mode='mps', layer_tags=None, dense=False, compress_opts=None, envs=None, **contract_boundary_opts)[source]¶

Compute the 1D boundary tensor networks describing the environments of rows and columns.

Parameters:

from_which ({'xmin', 'xmax', 'ymin', 'ymax'}) – Which boundary to compute the environments from.
xrange (tuple[int], optional) – The range of rows to compute the environments for.
yrange (tuple[int], optional) – The range of columns to compute the environments for.
max_bond (int, optional) – The maximum bond dimension of the environments.
cutoff (float, optional) – The cutoff for the singular values of the environments.
canonize (bool, optional) – Whether to canonicalize along each MPS environment before compressing.
mode ({'mps', 'projector', 'full-bond'}, optional) – Which contraction method to use for the environments.
layer_tags (str or iterable[str], optional) – If this 2D TN is multi-layered (e.g. a bra and a ket), and mode == 'mps', contract and compress each specified layer separately, for a cheaper contraction.
dense (bool, optional) – Whether to use dense tensors for the environments.
compress_opts (dict, optional) – Other options to pass to tensor_compress_bond().
envs (dict, optional) – An existing dictionary to store the environments in.
contract_boundary_opts – Other options to pass to contract_boundary_from().

Returns:

envs – A dictionary of the environments, with keys of the form (from_which, row_or_col_index).

Return type:

dict

compute_xmin_environments[source]¶: Compute the self.Lx 1D boundary tensor networks describing the lower environments of each row in this 2D tensor network. See compute_x_environments() for full details.

compute_xmax_environments[source]¶: Compute the self.Lx 1D boundary tensor networks describing the upper environments of each row in this 2D tensor network. See compute_x_environments() for full details.

compute_ymin_environments[source]¶: Compute the self.Ly 1D boundary tensor networks describing the left environments of each column in this 2D tensor network. See compute_y_environments() for full details.

compute_ymax_environments[source]¶: Compute the self.Ly 1D boundary tensor networks describing the right environments of each column in this 2D tensor network. See compute_y_environments() for full details.

compute_x_environments(max_bond=None, *, cutoff=1e-10, canonize=True, dense=False, mode='mps', layer_tags=None, compress_opts=None, envs=None, **contract_boundary_opts)[source]¶

Compute the 2 * self.Lx 1D boundary tensor networks describing the lower and upper environments of each row in this 2D tensor network, assumed to represent the norm.

The top or ‘xmax’ environment for row i will be a contraction of all rows i + 1, i + 2, ... etc:

 ●━━━●━━━●━━━●━━━●━━━●━━━●━━━●━━━●━━━●
╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲

The bottom or ‘xmin’ environment for row i will be a contraction of all rows i - 1, i - 2, ... etc:

╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱
 ●━━━●━━━●━━━●━━━●━━━●━━━●━━━●━━━●━━━●

Such that envs['xmax', i] & self.select(self.x_tag(i)) & envs['xmin', i] would look like:

 ●━━━●━━━●━━━●━━━●━━━●━━━●━━━●━━━●━━━●
╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲
o─o─o─o─o─o─o─o─o─o─o─o─o─o─o─o─o─o─o─o
╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱ ╲ ╱
 ●━━━●━━━●━━━●━━━●━━━●━━━●━━━●━━━●━━━●

And be (an approximation of) the norm centered around row i

Parameters:

max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
canonize (bool, optional) – Whether to sweep one way with canonization before compressing.
dense (bool, optional) – If true, contract the boundary in as a single dense tensor.
mode ({'mps', 'full-bond'}, optional) – How to perform the boundary compression.
layer_tags (None or sequence[str], optional) – If None, all tensors at each coordinate pair [(i, j), (i + 1, j)] will be first contracted. If specified, then the outer tensor at (i, j) will be contracted with the tensor specified by [(i + 1, j), layer_tag], for each layer_tag in layer_tags.
compress_opts (None or dict, optional) – Supplied to compress_between().
envs (dict, optional) – Supply an existing dictionary to store the environments in.
contract_boundary_opts – Supplied to contract_boundary_from_xmin() and contract_boundary_from_xmax() .

Returns:

x_envs – The two environment tensor networks of row i will be stored in x_envs['xmin', i] and x_envs['xmax', i].

Return type:

dict[(str, int), TensorNetwork]

compute_y_environments(max_bond=None, *, cutoff=1e-10, canonize=True, dense=False, mode='mps', layer_tags=None, compress_opts=None, envs=None, **contract_boundary_opts)[source]¶

Compute the 2 * self.Ly 1D boundary tensor networks describing the left (‘ymin’) and right (‘ymax’) environments of each column in this 2D tensor network, assumed to represent the norm.

The left or ‘ymin’ environment for column j will be a contraction of all columns j - 1, j - 2, ... etc:

●<
┃
●<
┃
●<
┃
●<

The right or ‘ymax’ environment for row j will be a contraction of all rows j + 1, j + 2, ... etc:

>●
 ┃
>●
 ┃
>●
 ┃
>●

Such that envs['ymin', j] & self.select(self.y_tag(j)) & envs['ymax', j] would look like:

   ╱o
●< o| >●
┃  |o  ┃
●< o| >●
┃  |o  ┃
●< o| >●
┃  |o  ┃
●< o╱ >●

And be (an approximation of) the norm centered around column j

Parameters:

max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
canonize (bool, optional) – Whether to sweep one way with canonization before compressing.
dense (bool, optional) – If true, contract the boundary in as a single dense tensor.
mode ({'mps', 'full-bond'}, optional) – How to perform the boundary compression.
layer_tags (None or sequence[str], optional) – If None, all tensors at each coordinate pair [(i, j), (i + 1, j)] will be first contracted. If specified, then the outer tensor at (i, j) will be contracted with the tensor specified by [(i + 1, j), layer_tag], for each layer_tag in layer_tags.
compress_opts (None or dict, optional) – Supplied to compress_between().
contract_boundary_opts – Supplied to contract_boundary_from_ymin() and contract_boundary_from_ymax() .

Returns:

y_envs – The two environment tensor networks of column j will be stored in y_envs['ymin', j] and y_envs['ymax', j].

Return type:

dict[(str, int), TensorNetwork]

_compute_plaquette_environments_x_first(x_bsz, y_bsz, max_bond=None, cutoff=1e-10, canonize=True, layer_tags=None, second_dense=None, x_envs=None, **compute_environment_opts)[source]¶

_compute_plaquette_environments_y_first(x_bsz, y_bsz, max_bond=None, cutoff=1e-10, canonize=True, layer_tags=None, second_dense=None, y_envs=None, **compute_environment_opts)[source]¶

compute_plaquette_environments(x_bsz=2, y_bsz=2, max_bond=None, *, cutoff=1e-10, canonize=True, mode='mps', layer_tags=None, first_contract=None, second_dense=None, compress_opts=None, **compute_environment_opts)[source]¶

Compute all environments like:

second_dense=False   second_dense=True (& first_contract='columns')

  ●──●                  ╭───●───╮
 ╱│  │╲                 │  ╱ ╲  │
●─.  .─●    ┬           ●─ . . ─●    ┬
│      │    ┊ x_bsz     │       │    ┊ x_bsz
●─.  .─●    ┴           ●─ . . ─●    ┴
 ╲│  │╱                 │  ╲ ╱  │
  ●──●                  ╰───●───╯

  <-->                    <->
 y_bsz                   y_bsz

Use two boundary contractions sweeps.

Parameters:

x_bsz (int, optional) – The size of the plaquettes in the x-direction (number of rows).
y_bsz (int, optional) – The size of the plaquettes in the y-direction (number of columns).
max_bond (int, optional) – The maximum boundary dimension, AKA ‘chi’. The default of None means truncation is left purely to cutoff and is not recommended in 2D.
cutoff (float, optional) – Cut-off value to used to truncate singular values in the boundary contraction.
canonize (bool, optional) – Whether to sweep one way with canonization before compressing.
mode ({'mps', 'full-bond'}, optional) – How to perform the boundary compression.
layer_tags (None or sequence[str], optional) – If None, all tensors at each coordinate pair [(i, j), (i + 1, j)] will be first contracted. If specified, then the outer tensor at (i, j) will be contracted with the tensor specified by [(i + 1, j), layer_tag], for each layer_tag in layer_tags.
first_contract ({None, 'x', 'y'}, optional) – The environments can either be generated with initial sweeps in the row (‘x’) or column (‘y’) direction. Generally it makes sense to perform this approximate step in whichever is smaller (the default).
second_dense (None or bool, optional) – Whether to perform the second set of contraction sweeps (in the rotated direction from whichever first_contract is) using a dense tensor or boundary method. By default this is only turned on if the bsz in the corresponding direction is 1.
compress_opts (None or dict, optional) – Supplied to compress_between().
compute_environment_opts – Supplied to compute_y_environments() or compute_x_environments() .

Returns:

The plaquette environments. The key is two tuples of ints, the startings coordinate of the plaquette being the first and the size of the plaquette being the second pair.

Return type:

dict[((int, int), (int, int)), TensorNetwork]

coarse_grain_hotrg(direction, max_bond=None, cutoff=1e-10, lazy=False, equalize_norms=False, optimize='auto-hq', compress_opts=None, inplace=False)[source]¶

Coarse grain this tensor network in direction using HOTRG. This inserts oblique projectors between tensor pairs and then optionally contracts them into new sites for form a lattice half the size.

Parameters:

direction ({'x', 'y'}) – The direction to coarse grain in.
max_bond (int, optional) – The maximum bond dimension of the projector pairs inserted.
cutoff (float, optional) – The cutoff for the singular values of the projector pairs.
lazy (bool, optional) – Whether to contract the coarse graining projectors or leave them in the tensor network lazily. Default is to contract them.
equalize_norms (bool, optional) – Whether to equalize the norms of the tensors in the coarse grained lattice.
optimize (str, optional) – The optimization method to use when contracting the coarse grained lattice, if lazy=False.
compress_opts (None or dict, optional) – Supplied to insert_compressor_between_regions().
inplace (bool, optional) – Whether to perform the coarse graining in place.

Returns:

The coarse grained tensor network, with size halved in direction.

Return type:

coarse_grain_hotrg_[source]¶

contract_hotrg(max_bond=None, *, cutoff=1e-10, canonize=False, canonize_opts=None, sequence=('x', 'y'), max_separation=1, max_unfinished=1, lazy=False, equalize_norms=False, final_contract=True, final_contract_opts=None, progbar=False, inplace=False, **coarse_grain_opts)[source]¶

Contract this tensor network using the finite version of HOTRG. See https://arxiv.org/abs/1201.1144v4 and https://arxiv.org/abs/1905.02351 for the more optimal computaton of the projectors used here. The TN is contracted sequentially in sequence directions by inserting oblique projectors between plaquettes, and then optionally contracting these new effective sites. The algorithm stops when only one direction has a length larger than 2, and thus exact contraction can be used.

Parameters:

max_bond (int, optional) – The maximum bond dimension of the projector pairs inserted.
cutoff (float, optional) – The cutoff for the singular values of the projector pairs.
canonize (bool, optional) – Whether to canonize all tensors before each contraction, via gauge_all().
canonize_opts (None or dict, optional) – Additional options to pass to gauge_all().
sequence (tuple of str, optional) – The directions to contract in. Default is to contract in all directions.
max_separation (int, optional) – The maximum distance between sides (i.e. length - 1) of the tensor network before that direction is considered finished.
max_unfinished (int, optional) – The maximum number of directions that can be unfinished (i.e. are still longer than max_separation + 1) before the coarse graining terminates.
lazy (bool, optional) – Whether to contract the coarse graining projectors or leave them in the tensor network lazily. Default is to contract them.
equalize_norms (bool or float, optional) – Whether to equalize the norms of all tensors after each contraction, gathering the overall scaling coefficient, log10, in tn.exponent.
final_contract (bool, optional) – Whether to exactly contract the remaining tensor network after the coarse graining contractions.
final_contract_opts (None or dict, optional) – Options to pass to contract(), optimize defaults to 'auto-hq'.
progbar (bool, optional) – Whether to show a progress bar.
inplace (bool, optional) – Whether to perform the coarse graining in place.
coarse_grain_opts – Additional options to pass to coarse_grain_hotrg().

Returns:

The contracted tensor network, which will have no more than one direction of length > 2.

Return type:

contract_hotrg_[source]¶

contract_ctmrg(max_bond=None, *, cutoff=1e-10, canonize=False, canonize_opts=None, lazy=False, mode='projector', compress_opts=None, sequence=None, xmin=None, xmax=None, ymin=None, ymax=None, max_separation=1, around=None, equalize_norms=False, final_contract=True, final_contract_opts=None, progbar=False, inplace=False, **contract_boundary_opts)[source]¶

Contract this 2D tensor network using the finite analog of the CTMRG algorithm - https://arxiv.org/abs/cond-mat/9507087. The TN is contracted sequentially in sequence directions by inserting oblique projectors between boundary pairs, and then optionally contracting these new effective sites. The algorithm stops when only one direction has a length larger than 2, and thus exact contraction can be used.

Parameters:

max_bond (int, optional) – The maximum bond dimension of the projector pairs inserted.
cutoff (float, optional) – The cutoff for the singular values of the projector pairs.
canonize (bool, optional) – Whether to canonize the boundary tensors before each contraction, via gauge_all().
canonize_opts (None or dict, optional) – Additional options to pass to gauge_all().
lazy (bool, optional) – Whether to contract the coarse graining projectors or leave them in the tensor network lazily. Default is to contract them.
mode (str, optional) – The method to perform the boundary contraction. Defaults to 'projector'.
compress_opts (None or dict, optional) – Other low level options to pass to insert_compressor_between_regions().
sequence (sequence of {'xmin', 'xmax', 'ymin', 'ymax'}, optional) – Which directions to cycle throught when performing the inwards contractions, i.e. from that direction. If around is specified you will likely need all of these! Default is to contract in all directions.
xmin (int, optional) – The initial bottom boundary row, defaults to 0.
xmax (int, optional) – The initial top boundary row, defaults to Lx - 1.
ymin (int, optional) – The initial left boundary column, defaults to 0.
ymax (int, optional) – The initial right boundary column, defaults to Ly - 1..
max_separation (int, optional) – If around is None, when any two sides become this far apart simply contract the remaining tensor network.
around (None or sequence of (int, int), optional) – If given, don’t contract the square of sites bounding these coordinates.
equalize_norms (bool or float, optional) – Whether to equalize the norms of the boundary tensors after each contraction, gathering the overall scaling coefficient, log10, in tn.exponent.
final_contract (bool, optional) – Whether to exactly contract the remaining tensor network after the boundary contraction.
final_contract_opts (None or dict, optional) – Options to pass to contract(), optimize defaults to 'auto-hq'.
progbar (bool, optional) – Whether to show a progress bar.
inplace (bool, optional) – Whether to perform the boundary contraction in place.
contract_boundary_opts – Additional options to pass to contract_boundary_from().

Returns:

Either the fully contracted scalar (if final_contract=True and around=None) or the partially contracted tensor network.

Return type:

scalar or TensorNetwork2D

contract_ctmrg_[source]¶

quimb.tensor.gen_2d_bonds(Lx, Ly, steppers=None, coo_filter=None, cyclic=False)[source]¶

Convenience function for tiling pairs of bond coordinates on a 2D lattice given a function like lambda i, j: (i + 1, j + 1).

Parameters:

Lx (int) – The number of rows.
Ly (int) – The number of columns.
steppers (callable or sequence of callable, optional) – Function(s) that take args (i, j) and generate another coordinate, thus defining a bond. Only valid steps are taken. If not given, defaults to nearest neighbor bonds.
coo_filter (callable) – Function that takes args (i, j) and only returns True if this is to be a valid starting coordinate.

Yields:

bond (tuple[tuple[int, int], tuple[int, int]]) – A pair of coordinates.

Examples

Generate nearest neighbor bonds:

>>> for bond in gen_2d_bonds(2, 2, [lambda i, j: (i, j + 1),
>>>                                 lambda i, j: (i + 1, j)]):
>>>     print(bond)
((0, 0), (0, 1))
((0, 0), (1, 0))
((0, 1), (1, 1))
((1, 0), (1, 1))

Generate next nearest neighbor digonal bonds:

>>> for bond in gen_2d_bonds(2, 2, [lambda i, j: (i + 1, j + 1),
>>>                                 lambda i, j: (i + 1, j - 1)]):
>>>     print(bond)
((0, 0), (1, 1))
((0, 1), (1, 0))

class quimb.tensor.TEBD2D(psi0, ham, tau=0.01, D=None, chi=None, imag=True, gate_opts=None, ordering=None, second_order_reflect=False, compute_energy_every=None, compute_energy_final=True, compute_energy_opts=None, compute_energy_fn=None, compute_energy_per_site=False, callback=None, keep_best=False, progbar=True)[source]¶

Bases: quimb.tensor.tensor_arbgeom_tebd.TEBDGen

Generic class for performing two dimensional time evolving block decimation, i.e. applying the exponential of a Hamiltonian using a product formula that involves applying local exponentiated gates only.

Parameters:

psi0 (TensorNetwork2DVector) – The initial state.
ham (LocalHam2D) – The Hamtiltonian consisting of local terms.
tau (float, optional) – The default local exponent, if considered as time real values here imply imaginary time.
max_bond ({'psi0', int, None}, optional) – The maximum bond dimension to keep when applying each gate.
gate_opts (dict, optional) – Supplied to quimb.tensor.tensor_2d.TensorNetwork2DVector.gate(), in addition to max_bond. By default contract is set to ‘reduce-split’ and cutoff is set to 0.0.
ordering (str, tuple[tuple[int]], callable, optional) – How to order the terms, if a string is given then use this as the strategy given to get_auto_ordering(). An explicit list of coordinate pairs can also be given. The default is to greedily form an ‘edge coloring’ based on the sorted list of Hamiltonian pair coordinates. If a callable is supplied it will be used to generate the ordering before each sweep.
second_order_reflect (bool, optional) – If True, then apply each layer of gates in ordering forward with half the time step, then the same with reverse order.
compute_energy_every (None or int, optional) – How often to compute and record the energy. If a positive integer ‘n’, the energy is computed before every nth sweep (i.e. including before the zeroth).
compute_energy_final (bool, optional) – Whether to compute and record the energy at the end of the sweeps regardless of the value of compute_energy_every. If you start sweeping again then this final energy is the same as the zeroth of the next set of sweeps and won’t be recomputed.
compute_energy_opts (dict, optional) – Supplied to compute_local_expectation(). By default max_bond is set to max(8, D**2) where D is the maximum bond to use for applying the gate, cutoff is set to 0.0 and normalized is set to True.
compute_energy_fn (callable, optional) – Supply your own function to compute the energy, it should take the TEBD2D object as its only argument.
callback (callable, optional) – A custom callback to run after every sweep, it should take the TEBD2D object as its only argument. If it returns any value that boolean evaluates to True then terminal the evolution.
progbar (boolean, optional) – Whether to show a live progress bar during the evolution.
kwargs – Extra options for the specific TEBD2D subclass.

state¶

The current state.

Type:: TensorNetwork2DVector

ham¶

The Hamiltonian being used to evolve.

Type:: LocalHam2D

energy¶

The current of the current state, this will trigger a computation if the energy at this iteration hasn’t been computed yet.

Type:: float

energies¶

The energies that have been computed, if any.

Type:: list[float]

its¶

The corresponding sequence of iteration numbers that energies have been computed at.

Type:: list[int]

taus¶

The corresponding sequence of time steps that energies have been computed at.

Type:: list[float]

best¶

If keep_best was set then the best recorded energy and the corresponding state that was computed - keys 'energy' and 'state' respectively.

Type:: dict

compute_energy()[source]¶: Compute and return the energy of the current state.

property chi¶

__repr__()[source]¶

class quimb.tensor.FullUpdate(psi0, ham, tau=0.01, D=None, chi=None, fit_strategy='als', fit_opts=None, compute_envs_every=1, pre_normalize=True, condition_tensors=True, condition_balance_bonds=True, contract_optimize='auto-hq', imag=True, gate_opts=None, ordering=None, second_order_reflect=False, compute_energy_every=None, compute_energy_final=True, compute_energy_opts=None, compute_energy_fn=None, compute_energy_per_site=False, callback=None, keep_best=False, progbar=True)[source]¶

Bases: TEBD2D

Implements the ‘Full Update’ version of 2D imaginary time evolution, where each application of a gate is fitted to the current tensors using a boundary contracted environment.

Parameters:

psi0 (TensorNetwork2DVector) – The initial state.
ham (LocalHam2D) – The Hamtiltonian consisting of local terms.
tau (float, optional) – The default local exponent, if considered as time real values here imply imaginary time.
max_bond ({'psi0', int, None}, optional) – The maximum bond dimension to keep when applying each gate.
gate_opts (dict, optional) – Supplied to quimb.tensor.tensor_2d.TensorNetwork2DVector.gate(), in addition to max_bond. By default contract is set to ‘reduce-split’ and cutoff is set to 0.0.
ordering (str, tuple[tuple[int]], callable, optional) – How to order the terms, if a string is given then use this as the strategy given to get_auto_ordering(). An explicit list of coordinate pairs can also be given. The default is to greedily form an ‘edge coloring’ based on the sorted list of Hamiltonian pair coordinates. If a callable is supplied it will be used to generate the ordering before each sweep.
second_order_reflect (bool, optional) – If True, then apply each layer of gates in ordering forward with half the time step, then the same with reverse order.
compute_energy_every (None or int, optional) – How often to compute and record the energy. If a positive integer ‘n’, the energy is computed before every nth sweep (i.e. including before the zeroth).
compute_energy_final (bool, optional) – Whether to compute and record the energy at the end of the sweeps regardless of the value of compute_energy_every. If you start sweeping again then this final energy is the same as the zeroth of the next set of sweeps and won’t be recomputed.
compute_energy_opts (dict, optional) – Supplied to compute_local_expectation(). By default max_bond is set to max(8, D**2) where D is the maximum bond to use for applying the gate, cutoff is set to 0.0 and normalized is set to True.
compute_energy_fn (callable, optional) – Supply your own function to compute the energy, it should take the TEBD2D object as its only argument.
callback (callable, optional) – A custom callback to run after every sweep, it should take the TEBD2D object as its only argument. If it returns any value that boolean evaluates to True then terminal the evolution.
progbar (boolean, optional) – Whether to show a live progress bar during the evolution.
fit_strategy ({'als', 'autodiff-fidelity'}, optional) –
Core method used to fit the gate application.
- 'als': alternating least squares
- 'autodiff-fidelity': local fidelity using autodiff
fit_opts (dict, optional) – Advanced options for the gate application fitting functions. Defaults are inserted and can be accessed via the .fit_opts attribute.
compute_envs_every ({'term', 'group', 'sweep', int}, optional) –
How often to recompute the environments used to the fit the gate application:
- 'term': every gate
- 'group': every set of commuting gates (the default)
- 'sweep': every total sweep
- int: every x number of total sweeps
pre_normalize (bool, optional) – Actively renormalize the state using the computed environments.
condition_tensors (bool, optional) – Whether to actively equalize tensor norms for numerical stability.
condition_balance_bonds (bool, optional) – If and when equalizing tensor norms, whether to also balance bonds as an additional conditioning.
contract_optimize (str, optional) – Contraction path optimizer to use for gate + env + sites contractions.

state¶

The current state.

Type:: TensorNetwork2DVector

ham¶

The Hamiltonian being used to evolve.

Type:: LocalHam2D

energy¶

The current of the current state, this will trigger a computation if the energy at this iteration hasn’t been computed yet.

Type:: float

energies¶

The energies that have been computed, if any.

Type:: list[float]

its¶

The corresponding sequence of iteration numbers that energies have been computed at.

Type:: list[int]

taus¶

The corresponding sequence of time steps that energies have been computed at.

Type:: list[float]

best¶

If keep_best was set then the best recorded energy and the corresponding state that was computed - keys 'energy' and 'state' respectively.

Type:: dict

fit_opts¶

Detailed options for fitting the applied gate.

Type:: dict

property fit_strategy¶

fit_opts¶

pre_normalize = True¶

contract_optimize = ''¶

condition_tensors = True¶

condition_balance_bonds = True¶

property compute_envs_every¶

set_state(psi)[source]¶: The default method for setting the current state - simply a copy. Subclasses can override this to perform additional transformations.

_maybe_compute_plaquette_envs(force=False)[source]¶: Compute and store the plaquette environments for all local terms.

presweep(i)[source]¶: Full update presweep - compute envs and inject gate options.

compute_energy()[source]¶: Full update compute energy - use the (likely) already calculated plaquette environments.

gate(G, where)[source]¶: Apply the gate G at sites where, using a fitting method that takes into account the current environment.

class quimb.tensor.LocalHam2D(Lx, Ly, H2, H1=None, cyclic=False)[source]¶

Bases: quimb.tensor.tensor_arbgeom_tebd.LocalHamGen

A 2D Hamiltonian represented as local terms. This combines all two site and one site terms into a single interaction per lattice pair, and caches operations on the terms such as getting their exponential.

Parameters:

Lx (int) – The number of rows.
Ly (int) – The number of columns.
H2 (array_like or dict[tuple[tuple[int]], array_like]) – The two site term(s). If a single array is given, assume to be the default interaction for all nearest neighbours. If a dict is supplied, the keys should represent specific pairs of coordinates like ((ia, ja), (ib, jb)) with the values the array representing the interaction for that pair. A default term for all remaining nearest neighbours interactions can still be supplied with the key None.
H1 (array_like or dict[tuple[int], array_like], optional) – The one site term(s). If a single array is given, assume to be the default onsite term for all terms. If a dict is supplied, the keys should represent specific coordinates like (i, j) with the values the array representing the local term for that site. A default term for all remaining sites can still be supplied with the key None.

terms¶

The total effective local term for each interaction (with single site terms appropriately absorbed). Each key is a pair of coordinates ija, ijb with ija < ijb.

Type:: dict[tuple[tuple[int]], array_like]

Lx¶

Ly¶

property nsites¶: The number of sites in the system.

__repr__()[source]¶

draw(ordering='sort', show_norm=True, figsize=None, fontsize=8, legend=True, ax=None, **kwargs)[source]¶

Plot this Hamiltonian as a network.

Parameters:

ordering ({'sort', None, 'random'}, optional) – An ordering of the termns, or an argument to be supplied to quimb.tensor.tensor_2d_tebd.LocalHam2D.get_auto_ordering() to generate this automatically.
show_norm (bool, optional) – Show the norm of each term as edge labels.
figsize (None or tuple[int], optional) – Size of the figure, defaults to size of Hamiltonian.
fontsize (int, optional) – Font size for norm labels.
legend (bool, optional) – Whether to show the legend of which terms are in which group.
ax (None or matplotlib.Axes, optional) – Add to a existing set of axes.

graph[source]¶

class quimb.tensor.SimpleUpdate(psi0, ham, tau=0.01, D=None, chi=None, gauge_renorm=True, gauge_smudge=1e-06, condition_tensors=True, condition_balance_bonds=True, long_range_use_swaps=False, long_range_path_sequence='random', imag=True, gate_opts=None, ordering=None, second_order_reflect=False, compute_energy_every=None, compute_energy_final=True, compute_energy_opts=None, compute_energy_fn=None, compute_energy_per_site=False, callback=None, keep_best=False, progbar=True)[source]¶

Bases: TEBD2D

A simple subclass of TEBD2D that overrides two key methods in order to keep ‘diagonal gauges’ living on the bonds of a PEPS. The gauges are stored separately from the main PEPS in the gauges attribute. Before and after a gate is applied they are absorbed and then extracted. When accessing the state attribute they are automatically inserted or you can call get_state(absorb_gauges=False) to lazily add them as hyperedge weights only. Reference: https://arxiv.org/abs/0806.3719.

Parameters:

psi0 (TensorNetwork2DVector) – The initial state.
ham (LocalHam2D) – The Hamtiltonian consisting of local terms.
tau (float, optional) – The default local exponent, if considered as time real values here imply imaginary time.
max_bond ({'psi0', int, None}, optional) – The maximum bond dimension to keep when applying each gate.
gate_opts (dict, optional) – Supplied to quimb.tensor.tensor_2d.TensorNetwork2DVector.gate(), in addition to max_bond. By default contract is set to ‘reduce-split’ and cutoff is set to 0.0.
ordering (str, tuple[tuple[int]], callable, optional) – How to order the terms, if a string is given then use this as the strategy given to get_auto_ordering(). An explicit list of coordinate pairs can also be given. The default is to greedily form an ‘edge coloring’ based on the sorted list of Hamiltonian pair coordinates. If a callable is supplied it will be used to generate the ordering before each sweep.
second_order_reflect (bool, optional) – If True, then apply each layer of gates in ordering forward with half the time step, then the same with reverse order.
compute_energy_every (None or int, optional) – How often to compute and record the energy. If a positive integer ‘n’, the energy is computed before every nth sweep (i.e. including before the zeroth).
compute_energy_final (bool, optional) – Whether to compute and record the energy at the end of the sweeps regardless of the value of compute_energy_every. If you start sweeping again then this final energy is the same as the zeroth of the next set of sweeps and won’t be recomputed.
compute_energy_opts (dict, optional) – Supplied to compute_local_expectation(). By default max_bond is set to max(8, D**2) where D is the maximum bond to use for applying the gate, cutoff is set to 0.0 and normalized is set to True.
compute_energy_fn (callable, optional) – Supply your own function to compute the energy, it should take the TEBD2D object as its only argument.
callback (callable, optional) – A custom callback to run after every sweep, it should take the TEBD2D object as its only argument. If it returns any value that boolean evaluates to True then terminal the evolution.
progbar (boolean, optional) – Whether to show a live progress bar during the evolution.
gauge_renorm (bool, optional) – Whether to actively renormalize the singular value gauges.
gauge_smudge (float, optional) – A small offset to use when applying the guage and its inverse to avoid numerical problems.
condition_tensors (bool, optional) – Whether to actively equalize tensor norms for numerical stability.
condition_balance_bonds (bool, optional) – If and when equalizing tensor norms, whether to also balance bonds as an additional conditioning.
long_range_use_swaps (bool, optional) – If there are long range terms, whether to use swap gates to apply the terms. If False, a long range blob tensor (which won’t scale well for long distances) is formed instead.
long_range_path_sequence (str or callable, optional) – If there are long range terms how to generate the path between the two coordinates. If callable, should take the two coordinates and return a sequence of coordinates that links them, else passed to gen_long_range_swap_path.

state¶

The current state.

Type:: TensorNetwork2DVector

ham¶

The Hamiltonian being used to evolve.

Type:: LocalHam2D

energy¶

The current of the current state, this will trigger a computation if the energy at this iteration hasn’t been computed yet.

Type:: float

energies¶

The energies that have been computed, if any.

Type:: list[float]

its¶

The corresponding sequence of iteration numbers that energies have been computed at.

Type:: list[int]

taus¶

The corresponding sequence of time steps that energies have been computed at.

Type:: list[float]

best¶

If keep_best was set then the best recorded energy and the corresponding state that was computed - keys 'energy' and 'state' respectively.

Type:: dict

gauge_renorm = True¶

gauge_smudge = 1e-06¶

condition_tensors = True¶

condition_balance_bonds = True¶

long_range_path_sequence = 'random'¶

_initialize_gauges()[source]¶: Create unit singular values, stored as tensors.

property gauges¶: The dictionary of bond pair coordinates to Tensors describing the weights (t = gauges[pair]; t.data) and index (t = gauges[pair]; t.inds[0]) of all the gauges.

property long_range_use_swaps¶

gate(U, where)[source]¶: Like TEBD2D.gate but absorb and extract the relevant gauges before and after each gate application.

get_state(absorb_gauges=True)[source]¶: Return the state, with the diagonal bond gauges either absorbed equally into the tensors on either side of them (absorb_gauges=True, the default), or left lazily represented in the tensor network with hyperedges (absorb_gauges=False).

set_state(psi)[source]¶: Set the wavefunction state, this resets the environment gauges to unity.

class quimb.tensor.PEPS3D(arrays, *, shape='urfdlbp', tags=None, site_ind_id='k{},{},{}', site_tag_id='I{},{},{}', x_tag_id='X{}', y_tag_id='Y{}', z_tag_id='Z{}', **tn_opts)[source]¶

Bases: TensorNetwork3DVector, TensorNetwork3DFlat

Projected Entangled Pair States object (3D).

Parameters:

arrays (sequence of sequence of sequence of array) – The core tensor data arrays.
shape (str, optional) – Which order the dimensions of the arrays are stored in, the default 'urfdlbp' stands for (‘up’, ‘right’, ‘front’, down’, ‘left’, ‘behind’, ‘physical’) meaning (x+, y+, z+, x-, y-, z-, physical) respectively. Arrays on the edge of lattice are assumed to be missing the corresponding dimension.
tags (set[str], optional) – Extra global tags to add to the tensor network.
site_ind_id (str, optional) – String specifier for naming convention of site indices.
site_tag_id (str, optional) – String specifier for naming convention of site tags.
x_tag_id (str, optional) – String specifier for naming convention of x-slice tags.
y_tag_id (str, optional) – String specifier for naming convention of y-slice tags.
z_tag_id (str, optional) – String specifier for naming convention of z-slice tags.

_EXTRA_PROPS = ('_site_tag_id', '_x_tag_id', '_y_tag_id', '_z_tag_id', '_Lx', '_Ly', '_Lz', '_site_ind_id')¶

_site_ind_id = 'k{},{},{}'¶

_site_tag_id = 'I{},{},{}'¶

_x_tag_id = 'X{}'¶

_y_tag_id = 'Y{}'¶

_z_tag_id = 'Z{}'¶

_Lx¶

_Ly¶

_Lz¶

permute_arrays(shape='urfdlbp')[source]¶

Permute the indices of each tensor in this PEPS3D to match shape. This doesn’t change how the overall object interacts with other tensor networks but may be useful for extracting the underlying arrays consistently. This is an inplace operation.

Parameters:: shape (str, optional) – A permutation of 'lrp' specifying the desired order of the left, right, and physical indices respectively.

classmethod from_fill_fn(fill_fn, Lx, Ly, Lz, bond_dim, phys_dim=2, cyclic=False, shape='urfdlbp', **peps3d_opts)[source]¶

Create a 3D PEPS from a filling function with signature fill_fn(shape).

Parameters:

Lx (int) – The number of x-slices.
Ly (int) – The number of y-slices.
Lz (int) – The number of z-slices.
bond_dim (int) – The bond dimension.
phys_dim (int, optional) – The physical index dimension.
shape (str, optional) – How to layout the indices of the tensors, the default is (up, right, front, down, left, back, phys) == 'urfdlbp'. This is the order of the shape supplied to the filling function.
peps_opts – Supplied to PEPS3D.

Returns:

psi

Return type:

classmethod empty(Lx, Ly, Lz, bond_dim, phys_dim=2, like='numpy', **peps3d_opts)[source]¶

Create an empty 3D PEPS.

Parameters:

Lx (int) – The number of x-slices.
Ly (int) – The number of y-slices.
Lz (int) – The number of z-slices.
bond_dim (int) – The bond dimension.
physical (int, optional) – The physical index dimension.
peps3d_opts – Supplied to PEPS3D.

Returns:

psi

Return type:

See also

PEPS3D.from_fill_fn

classmethod ones(Lx, Ly, Lz, bond_dim, phys_dim=2, like='numpy', **peps3d_opts)[source]¶

Create a 3D PEPS whose tensors are filled with ones.

Parameters:

Lx (int) – The number of x-slices.
Ly (int) – The number of y-slices.
Lz (int) – The number of z-slices.
bond_dim (int) – The bond dimension.
physical (int, optional) – The physical index dimension.
peps3d_opts – Supplied to PEPS3D.

Returns:

psi

Return type:

See also

PEPS3D.from_fill_fn

classmethod rand(Lx, Ly, Lz, bond_dim, phys_dim=2, dist='normal', loc=0.0, dtype='float64', seed=None, **peps3d_opts)[source]¶

Create a random (un-normalized) 3D PEPS.

Parameters:

Lx (int) – The number of x-slices.
Ly (int) – The number of y-slices.
Lz (int) – The number of z-slices.
bond_dim (int) – The bond dimension.
physical (int, optional) – The physical index dimension.
dtype (dtype, optional) – The dtype to create the arrays with, default is real double.
seed (int, optional) – A random seed.
peps_opts – Supplied to PEPS3D.

Returns:

psi

Return type:

See also

PEPS3D.from_fill_fn

partial_trace_cluster(keep, max_bond=None, *, cutoff=1e-10, max_distance=0, fillin=0, gauges=False, flatten=False, normalized=True, symmetrized='auto', get=None, **contract_boundary_opts)[source]¶

Compute the approximate reduced density matrix at sites where by contracting a local cluster of tensors, potentially gauging the region with the simple update style bond gauges in gauges.

Parameters:

where (sequence[node]) – The sites to keep.
gauges (dict[str, array_like], optional) – The store of gauge bonds, the keys being indices and the values being the vectors. Only bonds present in this dictionary will be used.
optimize (str or PathOptimizer, optional) – The contraction path optimizer to use, when exactly contracting the local tensors.
normalized (bool or "return", optional) – Whether to normalize the result. If “return”, return the norm separately.
max_distance (int, optional) – The maximum graph distance to include tensors neighboring where when computing the expectation. The default 0 means only the tensors at sites where are used, 1 includes there direct neighbors, etc.
mode ({'graphdistance', 'loopunion'}, optional) – How to select the local tensors, either by graph distance or by selecting the union of all loopy regions containing where, of size up to max_distance, ensuring no dangling tensors.
fillin (bool or int, optional) – When selecting the local tensors, whether and how many times to ‘fill-in’ corner tensors attached multiple times to the local region. On a lattice this fills in the corners. See select_local().
grow_from ({"all", "any"}, optional) – If mode is ‘loopunion’, whether each loop should contain all of the initial tagged tensors, or just any of them (generating a larger region).
smudge (float, optional) – A small value to add to the gauges before multiplying them in and inverting them to avoid numerical issues.
power (float, optional) – The power to raise the singular values to before multiplying them in and inverting them.
get ({'matrix', 'array', 'tensor'}, optional) – Whether to return the result as a fused matrix (i.e. always 2D), unfused array, or still labeled Tensor.
rehearse (bool, optional) – Whether to perform the computation or not, if True return a rehearsal info dict.
contract_opts – Supplied to contract().

partial_trace(keep, max_bond=None, *, cutoff=1e-10, canonize=True, flatten=False, normalized=True, symmetrized='auto', envs=None, storage_factory=None, boundary_order=None, contract_cell_optimize='auto-hq', contract_cell_method='boundary', contract_cell_opts=None, get=None, **contract_boundary_opts)[source]¶

Partially trace this tensor network state, keeping only the sites in keep, using compressed contraction.

Parameters:

keep (iterable of hashable) – The sites to keep.
max_bond (int) – The maximum bond dimensions to use while compressed contracting.
optimize (str or PathOptimizer, optional) – The contraction path optimizer to use, should specifically generate contractions paths designed for compressed contraction.
flatten ({False, True, 'all'}, optional) – Whether to force ‘flattening’ (contracting all physical indices) of the tensor network before contraction, whilst this makes the TN generally more complex to contract, the accuracy is usually improved. If 'all' also flatten the tensors in keep.
reduce (bool, optional) – Whether to first ‘pull’ the physical indices off their respective tensors using QR reduction. Experimental.
normalized (bool, optional) – Whether to normalize the reduced density matrix at the end.
symmetrized ({'auto', True, False}, optional) – Whether to symmetrize the reduced density matrix at the end. This should be unecessary if flatten is set to True.

rehearse ({False, 'tn', 'tree', True}, optional) –

Whether to perform the computation or not:

- False: perform the computation.
- 'tn': return the tensor network without running the path
  optimizer.
- 'tree': run the path optimizer and return the
  ``cotengra.ContractonTree``..
- True: run the path optimizer and return the ``PathInfo``.

contract_compressed_opts (dict, optional) – Additional keyword arguments to pass to contract_compressed().

Returns:

rho – The reduce density matrix of sites in keep.

Return type:

array_like

compute_local_expectation(terms, max_bond=None, *, cutoff=1e-10, canonize=True, flatten=False, normalized=True, symmetrized='auto', return_all=False, envs=None, storage_factory=None, progbar=False, **contract_boundary_opts)[source]¶

Compute the local expectations of many local operators, by approximately contracting the full overlap tensor network.

Parameters:

terms (dict[node or (node, node), array_like]) – The terms to compute the expectation of, with keys being the sites and values being the local operators.
max_bond (int) – The maximum bond dimension to use during contraction.
optimize (str or PathOptimizer) – The compressed contraction path optimizer to use.
method ({'rho', 'rho-reduced'}, optional) –
The method to use to compute the expectation value.
- ’rho’: compute the expectation value via the reduced density matrix.
- ’rho-reduced’: compute the expectation value via the reduced density matrix, having reduced the physical indices onto the bonds first.
flatten (bool, optional) – Whether to force ‘flattening’ (contracting all physical indices) of the tensor network before contraction, whilst this makes the TN generally more complex to contract, the accuracy can often be much improved.
normalized (bool, optional) – Whether to locally normalize the result.
symmetrized ({'auto', True, False}, optional) – Whether to symmetrize the reduced density matrix at the end. This should be unecessary if flatten is set to True.
return_all (bool, optional) – Whether to return all results, or just the summed expectation. If rehease is not False, this is ignored and a dict is always returned.

rehearse ({False, 'tn', 'tree', True}, optional) –

Whether to perform the computations or not:

- False: perform the computation.
- 'tn': return the tensor networks of each local expectation,
  without running the path optimizer.
- 'tree': run the path optimizer and return the
  ``cotengra.ContractonTree`` for each local expectation.
- True: run the path optimizer and return the ``PathInfo`` for
  each local expectation.

executor (Executor, optional) – If supplied compute the terms in parallel using this executor.
progbar (bool, optional) – Whether to show a progress bar.
contract_compressed_opts – Supplied to contract_compressed().

Returns:

expecs – If return_all==False, return the summed expectation value of the given terms. Otherwise, return a dictionary mapping each term’s location to the expectation value.

Return type:

float or dict[node or (node, node), float]

class quimb.tensor.TensorNetwork3D(ts=(), *, virtual=False, check_collisions=True)[source]¶

Bases: quimb.tensor.tensor_arbgeom.TensorNetworkGen

Mixin class for tensor networks with a cubic lattice three-dimensional structure.

_NDIMS = 3¶

_EXTRA_PROPS = ('_site_tag_id', '_x_tag_id', '_y_tag_id', '_z_tag_id', '_Lx', '_Ly', '_Lz')¶

_compatible_3d(other)[source]¶: Check whether self and other are compatible 3D tensor networks such that they can remain a 3D tensor network when combined.

combine(other, *, virtual=False, check_collisions=True)[source]¶

Combine this tensor network with another, returning a new tensor network. If the two are compatible, cast the resulting tensor network to a TensorNetwork3D instance.

Parameters:

other (TensorNetwork3D or TensorNetwork) – The other tensor network to combine with.
virtual (bool, optional) – Whether the new tensor network should copy all the incoming tensors (False, the default), or view them as virtual (True).
check_collisions (bool, optional) – Whether to check for index collisions between the two tensor networks before combining them. If True (the default), any inner indices that clash will be mangled.

Return type:

TensorNetwork3D or TensorNetwork

property Lx¶: The number of x-slices.

property Ly¶: The number of y-slices.

property Lz¶: The number of z-slices.

property nsites¶: The total number of sites.

site_tag(i, j=None, k=None)[source]¶: The name of the tag specifiying the tensor at site (i, j, k).

property x_tag_id¶: The string specifier for tagging each x-slice of this 3D TN.

x_tag(i)[source]¶

property x_tags¶: A tuple of all of the Lx different x-slice tags.

property y_tag_id¶: The string specifier for tagging each y-slice of this 3D TN.

y_tag(j)[source]¶

property y_tags¶: A tuple of all of the Ly different y-slice tags.

property z_tag_id¶: The string specifier for tagging each z-slice of this 3D TN.

z_tag(k)[source]¶

property z_tags¶: A tuple of all of the Lz different z-slice tags.

maybe_convert_coo(coo)[source]¶: Check if coo is a tuple of three ints and convert to the corresponding site tag if so.

_get_tids_from_tags(tags, which='all')[source]¶: This is the function that lets coordinates such as (i, j, k) be used for many ‘tag’ based functions.

gen_site_coos()[source]¶: Generate coordinates for all the sites in this 3D TN.

gen_bond_coos()[source]¶: Generate pairs of coordinates for all the bonds in this 3D TN.

valid_coo(coo, xrange=None, yrange=None, zrange=None)[source]¶

Check whether coo is in-bounds.

Parameters:

coo ((int, int, int), optional) – The coordinates to check.
xrange ((int, int), optional) – The range of allowed values for the x, y, and z coordinates.
yrange ((int, int), optional) – The range of allowed values for the x, y, and z coordinates.
zrange ((int, int), optional) – The range of allowed values for the x, y, and z coordinates.

Return type:

bool

get_ranges_present()[source]¶

Return the range of site coordinates present in this TN.

Returns:: xrange, yrange, zrange – The minimum and maximum site coordinates present in each direction.
Return type:: tuple[tuple[int, int]]

Examples

>>> tn = qtn.TN3D_rand(4, 4, 4, 2)
>>> tn_sub = tn.select_local('I1,2,3', max_distance=1)
>>> tn_sub.get_ranges_present()
((0, 2), (1, 3), (2, 3))

is_cyclic_x(j=None, k=None, imin=None, imax=None)[source]¶: Check if the x dimension is cyclic (periodic), specifically whether a bond exists between (imin, j, k) and (imax, j, k), with default values of imin = 0 and imax = Lx - 1, and j and k the center of the lattice. If imin and imax are adjacent then this is considered False, since there is no ‘extra’ connectivity.

is_cyclic_y(k=None, i=None, jmin=None, jmax=None)[source]¶: Check if the y dimension is cyclic (periodic), specifically whether a bond exists between (i, jmin, k) and (i, jmax, k), with default values of jmin = 0 and jmax = Ly - 1, and i and k the center of the lattice. If jmin and jmax are adjacent then this is considered False, since there is no ‘extra’ connectivity.

is_cyclic_z(i=None, j=None, kmin=None, kmax=None)[source]¶: Check if the z dimension is cyclic (periodic), specifically whether a bond exists between (i, j, kmin) and (i, j, kmax), with default values of kmin = 0 and kmax = Lz - 1, and i and j the center of the lattice. If kmin and kmax are adjacent then this is considered False, since there is no ‘extra’ connectivity.

__getitem__(key)[source]¶: Key based tensor selection, checking for integer based shortcut.

_repr_info()[source]¶: General info to show in various reprs. Sublasses can add more relevant info to this dict.

flatten(fuse_multibonds=True, inplace=False)[source]¶: Contract all tensors corresponding to each site into one.

flatten_[source]¶

gen_pairs(xrange=None, yrange=None, zrange=None, xreverse=False, yreverse=False, zreverse=False, coordinate_order='xyz', xstep=None, ystep=None, zstep=None, stepping_order='xyz', step_only=None)[source]¶

Helper function for generating pairs of cooordinates for all bonds within a certain range, optionally specifying an order.

Parameters:

xrange ((int, int), optional) – The range of allowed values for the x, y, and z coordinates.
yrange ((int, int), optional) – The range of allowed values for the x, y, and z coordinates.
zrange ((int, int), optional) – The range of allowed values for the x, y, and z coordinates.
xreverse (bool, optional) – Whether to reverse the order of the x, y, and z sweeps.
yreverse (bool, optional) – Whether to reverse the order of the x, y, and z sweeps.
zreverse (bool, optional) – Whether to reverse the order of the x, y, and z sweeps.
coordinate_order (str, optional) – The order in which to sweep the x, y, and z coordinates. Earlier dimensions will change slower. If the corresponding range has size 1 then that dimension doesn’t need to be specified.
xstep (int, optional) – When generating a bond, step in this direction to yield the neighboring coordinate. By default, these follow xreverse, yreverse, and zreverse respectively.
ystep (int, optional) – When generating a bond, step in this direction to yield the neighboring coordinate. By default, these follow xreverse, yreverse, and zreverse respectively.
zstep (int, optional) – When generating a bond, step in this direction to yield the neighboring coordinate. By default, these follow xreverse, yreverse, and zreverse respectively.
stepping_order (str, optional) – The order in which to step the x, y, and z coordinates to generate bonds. Does not need to include all dimensions.
step_only (int, optional) – Only perform the ith steps in stepping_order, used to interleave canonizing and compressing for example.

Yields:

coo_a, coo_b (((int, int, int), (int, int, int)))

canonize_plane(xrange, yrange, zrange, equalize_norms=False, canonize_opts=None, **gen_pair_opts)[source]¶: Canonize every pair of tensors within a subrange, optionally specifying a order to visit those pairs in.

compress_plane(xrange, yrange, zrange, max_bond=None, cutoff=1e-10, equalize_norms=False, compress_opts=None, **gen_pair_opts)[source]¶: Compress every pair of tensors within a subrange, optionally specifying a order to visit those pairs in.

_contract_boundary_core_via_2d(xrange, yrange, zrange, from_which, max_bond, cutoff=1e-10, method='local-early', layer_tags=None, **compress_opts)[source]¶

_contract_boundary_core(xrange, yrange, zrange, from_which, max_bond, cutoff=1e-10, canonize=True, canonize_interleave=True, layer_tags=None, compress_late=True, equalize_norms=False, compress_opts=None, canonize_opts=None)[source]¶

_contract_boundary_projector(xrange, yrange, zrange, from_which, max_bond=None, cutoff=1e-10, canonize=False, layer_tags=None, lazy=False, equalize_norms=False, optimize='auto-hq', compress_opts=None, canonize_opts=None)[source]¶

contract_boundary_from(xrange, yrange, zrange, from_which, max_bond=None, *, cutoff=1e-10, mode='peps', equalize_norms=False, compress_opts=None, canonize_opts=None, inplace=False, **contract_boundary_opts)[source]¶: Unified entrypoint for contracting any rectangular patch of tensors from any direction, with any boundary method.

contract_boundary_from_[source]¶

_contract_interleaved_boundary_sequence(*, contract_boundary_opts=None, sequence=None, xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, max_separation=1, max_unfinished=1, around=None, equalize_norms=False, canonize=False, canonize_opts=None, final_contract=True, final_contract_opts=None, optimize='auto-hq', progbar=False, inplace=False)[source]¶: Unified handler for performing iterleaved contractions in a sequence of inwards boundary directions.

contract_boundary(max_bond=None, *, cutoff=1e-10, mode='peps', canonize=True, compress_opts=None, sequence=None, xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, max_separation=1, max_unfinished=1, around=None, equalize_norms=False, final_contract=True, final_contract_opts=None, optimize='auto-hq', progbar=False, inplace=False, **contract_boundary_opts)[source]¶

contract_boundary_[source]¶

contract_ctmrg(max_bond=None, *, cutoff=1e-10, canonize=False, canonize_opts=None, lazy=False, mode='projector', compress_opts=None, sequence=None, xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, max_separation=1, max_unfinished=1, around=None, equalize_norms=False, final_contract=True, final_contract_opts=None, progbar=False, inplace=False, **contract_boundary_opts)[source]¶

contract_ctmrg_[source]¶

_compute_plane_envs(xrange, yrange, zrange, from_which, envs=None, storage_factory=None, **contract_boundary_opts)[source]¶: Compute all ‘plane’ environments for the cube given by xrange, yrange, zrange, with direction given by from_which.

_maybe_compute_cell_env(key, envs=None, storage_factory=None, boundary_order=None, **contract_boundary_opts)[source]¶: Recursively compute the necessary 2D, 1D, and 0D environments.

coarse_grain_hotrg(direction, max_bond=None, cutoff=1e-10, lazy=False, equalize_norms=False, optimize='auto-hq', compress_opts=None, inplace=False)[source]¶

Coarse grain this tensor network in direction using HOTRG. This inserts oblique projectors between tensor pairs and then optionally contracts them into new sites for form a lattice half the size.

Parameters:

direction ({'x', 'y', 'z'}) – The direction to coarse grain in.
max_bond (int, optional) – The maximum bond dimension of the projector pairs inserted.
cutoff (float, optional) – The cutoff for the singular values of the projector pairs.
lazy (bool, optional) – Whether to contract the coarse graining projectors or leave them in the tensor network lazily. Default is to contract them.
equalize_norms (bool, optional) – Whether to equalize the norms of the tensors in the coarse grained lattice.
optimize (str, optional) – The optimization method to use when contracting the coarse grained lattice, if lazy=False.
compress_opts (None or dict, optional) – Supplied to insert_compressor_between_regions().
inplace (bool, optional) – Whether to perform the coarse graining in place.

Returns:

The coarse grained tensor network, with size halved in direction.

Return type:

See also

contract_hotrg, insert_compressor_between_regions

coarse_grain_hotrg_[source]¶

contract_hotrg(max_bond=None, cutoff=1e-10, canonize=False, canonize_opts=None, sequence=('x', 'y', 'z'), max_separation=1, max_unfinished=1, lazy=False, equalize_norms=False, final_contract=True, final_contract_opts=None, progbar=False, inplace=False, **coarse_grain_opts)[source]¶

Contract this tensor network using the finite version of HOTRG. See https://arxiv.org/abs/1201.1144v4 and https://arxiv.org/abs/1905.02351 for the more optimal computaton of the projectors used here. The TN is contracted sequentially in sequence directions by inserting oblique projectors between tensor pairs, and then optionally contracting these new effective sites. The algorithm stops when only one direction has a length larger than 2, and thus exact contraction can be used.

Parameters:

max_bond (int, optional) – The maximum bond dimension of the projector pairs inserted.
cutoff (float, optional) – The cutoff for the singular values of the projector pairs.
sequence (tuple of str, optional) – The directions to contract in. Default is to contract in all directions.
max_separation (int, optional) – The maximum distance between sides (i.e. length - 1) of the tensor network before that direction is considered finished.
max_unfinished (int, optional) – The maximum number of directions that can be unfinished (i.e. are still longer than max_separation + 1) before the coarse graining terminates.
lazy (bool, optional) – Whether to contract the coarse graining projectors or leave them in the tensor network lazily. Default is to contract them.
equalize_norms (bool or float, optional) – Whether to equalize the norms of the tensors in the tensor network after each coarse graining step.
final_contract (bool, optional) – Whether to exactly contract the remaining tensor network after the coarse graining contractions.
final_contract_opts (None or dict, optional) – Options to pass to contract(), optimize defaults to 'auto-hq'.
inplace (bool, optional) – Whether to perform the coarse graining in place.
coarse_grain_opts – Additional options to pass to coarse_grain_hotrg().

Returns:

The contracted tensor network, which will have no more than one directino of length > 2.

Return type:

See also

coarse_grain_hotrg, insert_compressor_between_regions

contract_hotrg_[source]¶

quimb.tensor.gen_3d_bonds(Lx, Ly, Lz, steppers=None, coo_filter=None, cyclic=False)[source]¶

Convenience function for tiling pairs of bond coordinates on a 3D lattice given a function like lambda i, j, k: (i + 1, j + 1, k + 1).

Parameters:

Lx (int) – The number of x-slices.
Ly (int) – The number of y-slices.
Lz (int) – The number of z-slices.
steppers (callable or sequence of callable) – Function(s) that take args (i, j, k) and generate another coordinate, thus defining a bond. Only valid steps are taken. If not given, defaults to nearest neighbor bonds.
coo_filter (callable) – Function that takes args (i, j, k) and only returns True if this is to be a valid starting coordinate.

Yields:

bond (tuple[tuple[int, int, int], tuple[int, int, int]]) – A pair of coordinates.

Examples

Generate nearest neighbor bonds:

>>> for bond in gen_3d_bonds(2, 2, 2, [lambda i, j, k: (i + 1, j, k),
...                                    lambda i, j, k: (i, j + 1, k),
...                                    lambda i, j, k: (i, j, k + 1)]):
...     print(bond)
((0, 0, 0), (1, 0, 0))
((0, 0, 0), (0, 1, 0))
((0, 0, 0), (0, 0, 1))
((0, 0, 1), (1, 0, 1))
((0, 0, 1), (0, 1, 1))
((0, 1, 0), (1, 1, 0))
((0, 1, 0), (0, 1, 1))
((0, 1, 1), (1, 1, 1))
((1, 0, 0), (1, 1, 0))
((1, 0, 0), (1, 0, 1))
((1, 0, 1), (1, 1, 1))
((1, 1, 0), (1, 1, 1))

class quimb.tensor.LocalHam3D(Lx, Ly, Lz, H2, H1=None, cyclic=False)[source]¶

Bases: quimb.tensor.tensor_arbgeom_tebd.LocalHamGen

Representation of a local hamiltonian defined on a general graph. This combines all two site and one site terms into a single interaction per lattice pair, and caches operations on the terms such as getting their exponential. The sites (nodes) should be hashable and comparable.

Parameters:

H2 (dict[tuple[node], array_like]) – The interaction terms, with each key being an tuple of nodes defining an edge and each value the local hamilotonian term for those two nodes.
H1 (array_like or dict[node, array_like], optional) – The one site term(s). If a single array is given, assume to be the default onsite term for all terms. If a dict is supplied, the keys should represent specific coordinates like (i, j) with the values the array representing the local term for that site. A default term for all remaining sites can still be supplied with the key None.

terms¶

The total effective local term for each interaction (with single site terms appropriately absorbed). Each key is a pair of coordinates site_a, site_b with site_a < site_b.

Type:: dict[tuple, array_like]

Lx¶

Ly¶

Lz¶

property nsites¶: The number of sites in the system.

__repr__()[source]¶

quimb.tensor.tensor_network_align(*tns: TensorNetworkGen, ind_ids=None, trace=False, inplace=False)[source]¶

Align an arbitrary number of tensor networks in a stack-like geometry:

a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a
| | | | | | | | | | | | | | | | | | <- ind_ids[0] (defaults to 1st id)
b-b-b-b-b-b-b-b-b-b-b-b-b-b-b-b-b-b
| | | | | | | | | | | | | | | | | | <- ind_ids[1]
               ...
| | | | | | | | | | | | | | | | | | <- ind_ids[-2]
y-y-y-y-y-y-y-y-y-y-y-y-y-y-y-y-y-y
| | | | | | | | | | | | | | | | | | <- ind_ids[-1]
z-z-z-z-z-z-z-z-z-z-z-z-z-z-z-z-z-z

Parameters:

tns (sequence of TensorNetwork) – The TNs to align, should be structured and either effective ‘vectors’ (have a site_ind_id) or ‘operators’ (have a up_ind_id and lower_ind_id).
ind_ids (None, or sequence of str) – String with format specifiers to id each level of sites with. Will be automatically generated like (tns[0].site_ind_id, "__ind_a{}__", "__ind_b{}__", ...) if not given.
inplace (bool) – Whether to modify the input tensor networks inplace.

Returns:

tns_aligned

Return type:

sequence of TensorNetwork

quimb.tensor.tensor_network_apply_op_op(A: TensorNetworkGenOperator, B: TensorNetworkGenOperator, which_A='lower', which_B='upper', contract=False, fuse_multibonds=True, compress=False, inplace=False, inplace_A=False, **compress_opts)[source]¶

Apply the operator (has upper and lower site inds) represented by tensor network A to the operator represented by tensor network B. The resulting tensor network has the same upper and lower indices as B. Optionally contract the tensors at each site, fuse any multibonds, and compress the resulting tensor network.

This is like performing A.to_dense() @ B.to_dense(), or various combinations of tranposes thereof, depending on the values of which_A and which_B.

Parameters:

A (TensorNetworkGenOperator) – The tensor network representing the operator to apply.
B (TensorNetworkGenOperator) – The tensor network representing the target operator.
which_A ({"lower", "upper"}, optional) – Whether to contract the lower or upper indices of A.
which_B ({"lower", "upper"}, optional) – Whether to contract the lower or upper indices of B.
contract (bool) – Whether to contract the tensors at each site after applying the operator, yielding a single tensor at each site.
fuse_multibonds (bool) – If contract=True, whether to fuse any multibonds after contracting the tensors at each site.
compress (bool) – Whether to compress the resulting tensor network.
inplace (bool) – Whether to modify B, the target tensor network inplace.
inplace_A (bool) – Whether to modify A, the applied operator tensor network inplace.
compress_opts – Options to pass to tn.compress, where tn is the resulting tensor network, if compress=True.

Returns:

The same type as B.

Return type:

TensorNetworkGenOperator

quimb.tensor.tensor_network_apply_op_vec(A: TensorNetworkGenOperator, x: TensorNetworkGenVector, which_A='lower', contract=False, fuse_multibonds=True, compress=False, inplace=False, inplace_A=False, **compress_opts)[source]¶

Apply a general a general tensor network representing an operator (has upper_ind_id and lower_ind_id) to a tensor network representing a vector (has site_ind_id), by contracting each pair of tensors at each site then compressing the resulting tensor network. How the compression takes place is determined by the type of tensor network passed in. The returned tensor network has the same site indices as x, and it is the lower_ind_id of A that is contracted.

This is like performing A.to_dense() @ x.to_dense(), or the transpose thereof, depending on the value of which_A.

Parameters:

A (TensorNetworkGenOperator) – The tensor network representing the operator.
x (TensorNetworkGenVector) – The tensor network representing the vector.
which_A ({"lower", "upper"}, optional) – Whether to contract the lower or upper indices of A with the site indices of x.
contract (bool) – Whether to contract the tensors at each site after applying the operator, yielding a single tensor at each site.
fuse_multibonds (bool) – If contract=True, whether to fuse any multibonds after contracting the tensors at each site.
compress (bool) – Whether to compress the resulting tensor network.
inplace (bool) – Whether to modify x, the input vector tensor network inplace.
inplace_A (bool) – Whether to modify A, the operator tensor network inplace.
compress_opts – Options to pass to tn.compress, where tn is the resulting tensor network, if compress=True.

Returns:

The same type as x.

Return type:

class quimb.tensor.LocalHamGen(H2, H1=None)[source]¶

Parameters:

H2 (dict[tuple[node], array_like]) – The interaction terms, with each key being an tuple of nodes defining an edge and each value the local hamilotonian term for those two nodes.
H1 (array_like or dict[node, array_like], optional) – The one site term(s). If a single array is given, assume to be the default onsite term for all terms. If a dict is supplied, the keys should represent specific coordinates like (i, j) with the values the array representing the local term for that site. A default term for all remaining sites can still be supplied with the key None.

terms¶

The total effective local term for each interaction (with single site terms appropriately absorbed). Each key is a pair of coordinates site_a, site_b with site_a < site_b.

Type:: dict[tuple, array_like]

_op_cache¶

terms¶

sites¶

_sites_to_covering_terms¶

property nsites¶: The number of sites in the system.

items()[source]¶: Iterate over all terms in the hamiltonian. This is mostly for convenient compatibility with compute_local_expectation.

_convert_from_qarray_cached(x)[source]¶

_flip_cached(x)[source]¶

_add_cached(x, y)[source]¶

_div_cached(x, y)[source]¶

_op_id_cached(x)[source]¶

_id_op_cached(x)[source]¶

_expm_cached(G, x)[source]¶

get_gate(where)[source]¶: Get the local term for pair where, cached.

get_gate_expm(where, x)[source]¶: Get the local term for pair where, matrix exponentiated by x, and cached.

apply_to_arrays(fn)[source]¶: Apply the function fn to all the arrays representing terms.

get_auto_ordering(order='sort', **kwargs)[source]¶

Get an ordering of the terms to use with TEBD, for example. The default is to sort the coordinates then greedily group them into commuting sets.

Parameters:

order ({'sort', None, 'random', str}) –

How to order the terms before greedily grouping them into commuting (non-coordinate overlapping) sets:

'sort' will sort the coordinate pairs first.

None will use the current order of terms which should match the order they were supplied to this LocalHam2D instance.

'random' will randomly shuffle the coordinate pairs before grouping them - not the same as returning a completely random order.

'random-ungrouped' will randomly shuffle the coordinate pairs but not group them at all with respect to commutation.

Any other option will be passed as a strategy to networkx.coloring.greedy_color() to generate the ordering.

Returns:

Sequence of coordinate pairs.

Return type:

list[tuple[node]]

__repr__()[source]¶

draw(ordering='sort', show_norm=True, figsize=None, fontsize=8, legend=True, ax=None, **kwargs)[source]¶

Plot this Hamiltonian as a network.

Parameters:

ordering ({'sort', None, 'random'}, optional) – An ordering of the termns, or an argument to be supplied to quimb.tensor.tensor_arbgeom_tebd.LocalHamGen.get_auto_ordering() to generate this automatically.
show_norm (bool, optional) – Show the norm of each term as edge labels.
figsize (None or tuple[int], optional) – Size of the figure, defaults to size of Hamiltonian.
fontsize (int, optional) – Font size for norm labels.
legend (bool, optional) – Whether to show the legend of which terms are in which group.
ax (None or matplotlib.Axes, optional) – Add to a existing set of axes.

graph[source]¶

class quimb.tensor.SimpleUpdateGen(psi0, ham, tau=0.01, D=None, cutoff=1e-10, imag=True, gate_opts=None, ordering=None, second_order_reflect=False, compute_energy_every=None, compute_energy_final=True, compute_energy_opts=None, compute_energy_fn=None, compute_energy_per_site=False, tol=None, equilibrate_every=0, equilibrate_start=True, equilibrate_opts=None, callback=None, keep_best=False, progbar=True)[source]¶

Bases: TEBDGen

Simple update for arbitrary geometry hamiltonians.

Parameters:

psi0 (TensorNetworkGenVector) – The initial state.
ham (LocalHamGen) – The local hamiltonian.
tau (float, optional) – The default time step to use.
D (int, optional) – The maximum bond dimension, by default the current maximum bond of psi0.
cutoff (float, optional) – The singular value cutoff to use when applying gates.
imag (bool, optional) – Whether to evolve in imaginary time (default) or real time.
gate_opts (dict, optional) – Other options to supply to the gate application method, quimb.tensor.tensor_arbgeom.TensorNetworkGenVector.gate_simple_().
ordering (None, str or callable, optional) – The ordering of the terms to apply, by default this will be determined automatically.
second_order_reflect (bool, optional) – Whether to use a second order Trotter decomposition by reflecting the ordering.
compute_energy_every (int, optional) – Compute the energy every this many steps.
compute_energy_final (bool, optional) – Whether to compute the energy at the end.
compute_energy_opts (dict, optional) – Options to supply to the energy computation method, quimb.tensor.tensor_arbgeom.TensorNetworkGenVector.compute_local_expectation_cluster().
compute_energy_fn (callable, optional) – A custom function to compute the energy, with signature fn(su), where su is this instance.
compute_energy_per_site (bool, optional) – Whether to compute the energy per site.
tol (float, optional) – If not None, stop when either energy difference falls below this value, or maximum singluar value changes fall below this value.
equilibrate_every (int, optional) – Equilibrate the gauges every this many steps.
equilibrate_start (bool, optional) – Whether to equilibrate the gauges at the start, regardless of equilibrate_every.
equilibrate_opts (dict, optional) – Default options to supply to the gauge equilibration method, see quimb.tensor.tensor_core.TensorNetwork.gauge_all_simple(). By default max_iterations is set to 100 and tol to 1e-3.
callback (callable, optional) – A function to call after each step, with signature fn(su).
keep_best (bool, optional) – Whether to keep track of the best state and energy.
progbar (bool, optional) – Whether to show a progress bar during evolution.

equilibrate_every = 0¶

equilibrate_start = True¶

equilibrate_opts¶

gauges_prev = None¶

gauge_diffs = []¶

gate(G, where)[source]¶: Application of a single gate G at where.

equilibrate(**kwargs)[source]¶: Equilibrate the gauges with the current state (like evolving with tau=0).

postlayer()[source]¶: Performed after each layer of commuting gates.

postsweep(i)[source]¶: Performed after every full sweep.

normalize()[source]¶: Normalize the state and simple gauges.

compute_energy()[source]¶: Default estimate of the energy.

get_state(absorb_gauges=True)[source]¶

Return the current state, possibly absorbing the gauges.

Parameters:

absorb_gauges (bool or "return", optional) – Whether to absorb the gauges into the state or not. If True, a standard PEPS is returned with the gauges absorbed. If False`, the gauges are added to the tensor network but uncontracted. If “return”, the gauges are returned separately.

Returns:

psi (TensorNetwork) – The current state.
gauges (dict) – The current gauges, if absorb_gauges == "return".

set_state(psi, gauges=None)[source]¶: Set the current state and possibly the gauges.

class quimb.tensor.TEBDGen(psi0, ham, tau=0.01, D=None, cutoff=1e-10, imag=True, gate_opts=None, ordering=None, second_order_reflect=False, compute_energy_every=None, compute_energy_final=True, compute_energy_opts=None, compute_energy_fn=None, compute_energy_per_site=False, tol=None, callback=None, keep_best=False, progbar=True)[source]¶

Generic class for performing time evolving block decimation on an arbitrary graph, i.e. applying the exponential of a Hamiltonian using a product formula that involves applying local exponentiated gates only.

imag = True¶

property state¶: Return a copy of the current state.

ham¶

progbar = True¶

callback = None¶

tau = 0.01¶

last_tau = 0.0¶

gate_opts¶

compute_energy_opts¶

compute_energy_every = None¶

compute_energy_final = True¶

compute_energy_fn = None¶

compute_energy_per_site = False¶

tol = None¶

second_order_reflect = False¶

_n = 0¶

its = []¶

taus = []¶

energies = []¶

energy_diffs = []¶

egrdm¶

keep_best = False¶

best¶

stop = False¶

sweep(tau)[source]¶: Perform a full sweep of gates at every pair.

\[\psi \rightarrow \prod_{\{ij\}} \exp(-\tau H_{ij}) \psi\]

_set_progbar_description(pbar)[source]¶

evolve(steps, tau=None, progbar=None)[source]¶: Evolve the state with the local Hamiltonian for steps steps with time step tau.

property n¶: The number of sweeps performed.

property D¶: The maximum bond dimension.

_check_energy()[source]¶: Logic for maybe computing the energy if needed.

property energy¶: Return the energy of current state, computing it only if necessary.

get_state()[source]¶: The default method for retrieving the current state - simply a copy. Subclasses can override this to perform additional transformations.

set_state(psi)[source]¶: The default method for setting the current state - simply a copy. Subclasses can override this to perform additional transformations.

presweep(i)[source]¶: Perform any computations required before the sweep (and energy computation). For the basic TEBD update is nothing.

postlayer()[source]¶: Perform any computations required after each layer of commuting gates. For the basic update this is nothing.

postsweep(i)[source]¶: Perform any computations required after the sweep (but before the energy computation). For the basic update this is nothing.

gate(U, where)[source]¶: Perform single gate U at coordinate pair where. This is the the most common method to override.

compute_energy()[source]¶: Compute and return the energy of the current state. Subclasses can override this with a custom method to compute the energy.

plot(zoom='auto', xscale='symlog', xscale_linthresh=20, color_energy=(0.0, 0.5, 1.0), color_gauge_diff=(1.0, 0.5, 0.0), hlines=(), figsize=(8, 4))[source]¶

Plot an overview of the evolution of the energy and gauge diffs.

Parameters:

zoom (int or 'auto', optional) – The number of iterations to zoom in on, or ‘auto’ to automatically choose a reasonable zoom level.
xscale ({'linear', 'log', 'symlog'}, optional) – The x-axis scale, for the upper plot of the entire evolution.
xscale_linthresh (float, optional) – The linear threshold for the upper symlog scale.
color_energy (str or tuple, optional) – The color to use for the energy plot.
color_gauge_diff (str or tuple, optional) – The color to use for the gauge diff plot.
hlines (dict, optional) – Add horizontal lines to the plot, with keys as labels and values as the y-values.
figsize (tuple, optional) – The size of the figure.

Returns:

fig, axs

Return type:

matplotlib.Figure, tuple[matplotlib.Axes]

__repr__()[source]¶

quimb.tensor.edge_coloring(edges, strategy='smallest_last', interchange=True, group=True)[source]¶

Generate an edge coloring for the graph given by edges, using networkx.coloring.greedy_color.

Parameters:

edges (sequence[tuple[hashable, hashable]]) – The edges of the graph.
strategy (str or callable, optional) –
The strategy to use for coloring the edges. Can be:
- ’largest_first’
- ’smallest_last’
- ’random_sequential’
…
interchange (bool, optional) – Whether to use the interchange heuristic. Usually generates better colorings but can be slower.
group (bool, optional) – Whether to group the edges by color or return a flat list.

quimb.tensor.MPS_COPY(L, phys_dim=2, dtype='float64', **mps_opts)[source]¶

Build a matrix product state representation of the COPY tensor.

Parameters:

L (int) – The number of sites.
phys_dim (int, optional) – The physical (site) dimensions, defaults to 2.
dtype (str or dtype, optional) – The data type of the tensor network, defaults to ‘float64’.

Return type:

quimb.tensor.HTN2D_classical_ising_partition_function(Lx, Ly, beta, h=0.0, j=1.0, cyclic=False, ind_id='s{},{}', site_tag_id='I{},{}')[source]¶

Hyper tensor network representation of the 2D classical ising model partition function. The indices will be shared by 4 or 5 tensors depending on whether h is non-zero. As opposed to the ‘normal’ tensor network, here each classical spin is still a single index, which is easier to contract exactly.

Parameters:

Lx (int) – Length of side x.
Ly (int) – Length of side y.
beta (float) – The inverse temperature.
h (float, optional) – The magnetic field strength.
j (float, dict, or callable, optional) – The interaction strength, positive being ferromagnetic. If a dict should contain entries for each edge, keyed by the edge. If a callable should have the signature j(node_a, node_b) and return a float.
cyclic (bool or (bool, bool), optional) – Whether to use periodic boundary conditions. X and Y can be specified separately using a tuple.
ind_id (str, optional) – How to label the indices i.e. ind_id.format(i, j), each of which corresponds to a single classical spin.
site_tag_id (str, optional) – How to label the site tags, note that in the hyper tensor network representation each tensor will have two site tags for the two sites it connects.

Return type:

quimb.tensor.HTN3D_classical_ising_partition_function(Lx, Ly, Lz, beta, j=1.0, h=0.0, cyclic=False, ind_id='s{},{},{}', site_tag_id='I{},{},{}')[source]¶

Hyper tensor network representation of the 3D classical ising model partition function. The indices will be shared by 6 or 7 tensors depending on whether h is non-zero. As opposed to the ‘normal’ tensor network, here each classical spin is still a single index, which is easier to contract exactly.

Parameters:

Lx (int) – Length of side x.
Ly (int) – Length of side y.
Lz (int) – Length of side z.
beta (float) – The inverse temperature.
j (float, dict, or callable, optional) – The interaction strength, positive being ferromagnetic. If a dict should contain entries for each edge, keyed by the edge. If a callable should have the signature j(node_a, node_b) and return a float.
h (float, optional) – The magnetic field strength.
cyclic (bool or (bool, bool, bool), optional) – Whether to use periodic boundary conditions. X, Y and Z can be specified separately using a tuple.
ind_id (str, optional) – How to label the indices i.e. ind_id.format(i, j, k), each of which corresponds to a single classical spin.
site_tag_id (str, optional) – How to label the site tags, note that in the hyper tensor network representation each tensor will have two site tags for the two sites it connects.

Return type:

quimb.tensor.HTN_classical_partition_function_from_edges(edges, beta, j=1.0, h=0.0, site_ind_id='s{}', site_tag_id='I{}', bond_tag_id='B{},{}')[source]¶

Build a hyper tensor network representation of a classical ising model partition function by specifying graph edges. There will be a single tensor per interaction rather than per site, as well as a single tensor for each site, if h != 0.0.

Parameters:

edges (sequence of tuple[hashable, hashable]) – The graph edges, as a sequence of pairs of hashable objects, for example integers, representing the nodes. You can redundantly specify (u, v) and (v, u) and only one edge will be added.
beta (float, optional) – The inverse temperature.
j (float, dict, or callable, optional) – The interaction strength, positive being ferromagnetic. If a dict should contain entries for each edge, keyed by the edge. If a callable should have the signature j(node_a, node_b) and return a float.
h (float, or callable, optional) – The magnetic field strength. If a callable should have the signature h(node) and return a float.
site_ind_id (str, optional) – A string formatter for naming tensor indices like site_ind_id.format(node).
site_tag_id (str, optional) – A string formatter for naming tensor tags like site_tag_id.format(node).
bond_tag_id (str, optional) – A string formatter for naming tensor tags like bond_tag_id.format(node_a, node_b).

Return type:

quimb.tensor.HTN_CP_from_sites_and_fill_fn(fill_fn, sites, D, phys_dim=2, site_tag_id='I{}', site_ind_id='k{}', bond_ind=None)[source]¶

Create a CP-decomposition structured hyper tensor network state from a sequence of sites and a fill function.

Parameters:

fill_fn (callable) – A function that takes a shape and returns an array.
sites (sequence of hashable) – The sites of the tensor network.
D (int) – The hyper bond dimension connecting tensors.
phys_dim (int, optional) – The size of the outer, physical indices.
site_tag_id (str, optional) – String with formatter to tag sites.
site_ind_id (str, optional) – String with formatter to tag indices (if phys_dim specified).

Return type:

quimb.tensor.HTN_dual_from_edges_and_fill_fn(fill_fn, edges, D, phys_dim=None, site_tag_id='I{}', site_ind_id='k{}')[source]¶: Create a hyper tensor network with a tensor on each bond and a hyper index on each node.

quimb.tensor.HTN_from_clauses(clauses, weights=None, mode='parafac', dtype='float64', clause_tag_id='CLAUSE{}', var_ind_id='var{}', weight_tag_id='WEIGHT{}')[source]¶

Given a list of clauses, create a hyper tensor network, with a single hyper index for each variable, and single tensor or tensor decomposition for each clause. If weights are given, there will also be a single tensor for each non-trivially weighted variable.

Parameters:

clauses (sequence of tuple[int]) – The clauses as a sequence of tuples of integers. Each integer represents a variable, and the sign indicates whether it is negated. The variables thus must be non-zero integers.
weights (dict[int, float], optional) – The weights for each variable. Each key should be a signed variable integer, such that relative weights for a variable v are (weights[-v], weights[v]). If only one is given of this pair, the other is assumed to sum to 1. If a variable is not supplied, or weights=None, then both weights are assumed to be 1 and no tensor is created for the variable.
mode ({'parafac', 'mps', 'dense', int}, optional) –
How to represent the clauses:
- ’parafac’ - N rank-2 tensors connected by a single hyper index. You could further call hyperinds_resolve() for more options to convert the hyper index into a (decomposed) COPY-tensor.
- ’mps’ - N rank-3 tensors connected along a 1D line.
- ’dense’ - contract the hyper index.
- int - use the ‘parafac’ mode, but only if the length of a clause is larger than this threshold.
Note that variables are always represented by a single (hyper) index, which is like an implicit PARAFAC decomposition.
dtype (str) – The data type of the tensors.
clause_tag_id (str) – The tag to use for the clause tensors. The tag will be formatted with the clause index.
var_ind_id (str) – The index to use for the variable tensors. The index will be formatted with the variable index.
weight_tag_id (str) – The tag to use for the weight tensors. The tag will be formatted with the variable index.

Returns:

htn

Return type:

quimb.tensor.HTN_from_cnf(fname, mode='parafac', dtype='float64', clause_tag_id='CLAUSE{}', var_ind_id='var{}', weight_tag_id='WEIGHT{}', **kwargs)[source]¶

Create a hyper tensor network from a ‘.cnf’ or ‘.wcnf’ file - i.e. a model counting or weighted model counting instance specification.

Parameters:

fname (str) – Path to a ‘.cnf’ or ‘.wcnf’ file.
mode ({'parafac', 'mps', 'dense', int}, optional) –
How to represent the clauses:
- ’parafac’ - N rank-2 tensors connected by a single hyper index. You could further call hyperinds_resolve() for more options to convert the hyper index into a (decomposed) COPY-tensor.
- ’mps’ - N rank-3 tensors connected along a 1D line.
- ’dense’ - contract the hyper index.
- int - use the ‘parafac’ mode, but only if the length of a clause is larger than this threshold.
dtype (str or dtype, optional) – Data type of the tensors.
clause_tag_id (str, optional) – Format string for clause tags.
var_ind_id (str, optional) – Format string for variable indices.
weight_tag_id (str, optional) – Format string for weight tags.
kwargs – Additional keyword arguments passed to HTN_from_clauses().

Returns:

htn

Return type:

quimb.tensor.HTN_rand(n, reg, n_out=0, n_hyper_in=0, n_hyper_out=0, d_min=2, d_max=3, seed=None, dtype='float64', dist='normal', scale=1.0, loc=0.0, site_ind_id='k{}')[source]¶

Create a random, possibly hyper, tensor network, with a mix of normal and hyper inner and outer indices. Useful for testing edges cases.

Parameters:

n (int) – The number of tensors.
reg (int) – The average degree (number of dimensions per tensor) of the tensor network (prior to placing extra indices).
n_out (int, optional) – The number of normal outer indices to add (i.e. appear exactly once).
n_hyper_in (int, optional) – The number of hyper inner indices to add (i.e. appear on three or more tensors).
n_hyper_out (int, optional) – The number of hyper outer indices to add (i.e. appear in output and two or more tensors).
d_min (int, optional) – The minimum size of any dimension.
d_max (int, optional) – The maximum size of any dimension.
seed (int, optional) – A random seed.

Return type:

quimb.tensor.HTN_random_ksat(k, num_variables, num_clauses=None, alpha=None, seed=None, allow_repeat_variables=False, mode='parafac', dtype='float64', clause_tag_id='CLAUSE{}', variable_ind_id='var{}')[source]¶

Create a random k-SAT instance encoded as a hyper tensor network.

Parameters:

k (int) – Number of variables per clause.
num_variables (int) – Number of variables in the instance.
num_clauses (int, optional) – Number of clauses in the instance. If not specified, will be determined from alpha.
alpha (float, optional) – If num_clauses is not directly specified then the average number of clauses per variable. Taken as a Poisson parameter. Either this or num_clauses must be specified.
seed (int, optional) – Random seed.
allow_repeat_variables (bool, optional) – Whether to allow the same variable to appear multiple times in a single clause.
mode ({'parafac', 'mps', 'dense', int}, optional) –
How to represent the clauses:
- ’parafac’ - N rank-2 tensors connected by a single hyper index. You could further call hyperinds_resolve() for more options to convert the hyper index into a (decomposed) COPY-tensor.
- ’mps’ - N rank-3 tensors connected along a 1D line.
- ’dense’ - contract the hyper index.
- int - use the ‘parafac’ mode, but only if the length of a clause is larger than this threshold.
Note that variables are always represented by a single (hyper) index, which is like an implicit PARAFAC decomposition.
dtype (str, optional) – Data type of the tensors.
clause_tag_id (str, optional) – Format string for clause tags. Should contain a single {} which will be replaced by the clause number.
variable_ind_id (str, optional) – Format string for variable indices. Should contain a single {} which will be replaced by the variable number.

Return type:

quimb.tensor.MPO_ham_heis(L, j=1.0, bz=0.0, *, S=1 / 2, cyclic=False, **mpo_opts)[source]¶

Heisenberg Hamiltonian in MPO form.

\[H_\mathrm{Heis} = \sum_{i} ( J_X S^X_i S^X_{i + 1} + J_Y S^Y_i S^Y_{i + 1} + J_Z S^Z_i S^Z_{i + 1} ) - B_Z \sum_{i} S^Z_i\]

Note the default convention of antiferromagnetic interactions and spin operators not Pauli matrices.

Parameters:

L (int) – The number of sites.
j (float or (float, float, float), optional) – The XX, YY and ZZ interaction strength. Positive is antiferromagnetic.
bz (float, optional) – The Z-magnetic field strength.
S ({1/2, 1, 3/2, ...}, optional) – The underlying spin of the system, defaults to 1/2.
cyclic (bool, optional) – Generate a MPO with periodic boundary conditions or not, default is open boundary conditions.
mpo_opts – Supplied to MatrixProductOperator.

Return type:

quimb.tensor.MPO_ham_ising(L, j=1.0, bx=0.0, *, S=1 / 2, cyclic=False, **mpo_opts)[source]¶

Ising Hamiltonian in MPO form.

\[H_\mathrm{Ising} = J \sum_{i} S^Z_i S^Z_{i + 1} - B_x \sum_{i} S^X_i\]

Note the default convention of antiferromagnetic interactions and spin operators not Pauli matrices.

Parameters:

L (int) – The number of sites.
j (float, optional) – The ZZ interaction strength. Positive is antiferromagnetic.
bx (float, optional) – The X-magnetic field strength.
S ({1/2, 1, 3/2, ...}, optional) – The underlying spin of the system, defaults to 1/2.
cyclic (bool, optional) – Generate a MPO with periodic boundary conditions or not, default is open boundary conditions.
local_ham_1d_opts (mpo_opts or) – Supplied to MatrixProductOperator.

Return type:

quimb.tensor.MPO_ham_mbl(L, dh, j=1.0, seed=None, S=1 / 2, *, cyclic=False, dh_dist='s', dh_dim=1, beta=None, **mpo_opts)[source]¶

The many-body-localized spin hamiltonian in MPO form.

\[H_\mathrm{MBL} = \sum_{i} ( J_X S^X_i S^X_{i + 1} + J_Y S^Y_i S^Y_{i + 1} + J_Z S^Z_i S^Z_{i + 1} ) - \sum_{i} h_i S^Z_i\]

Note the default convention of antiferromagnetic interactions and spin operators not Pauli matrices.

Parameters:

L (int) – Number of spins.
dh (float) – Random noise strength.
j (float, or (float, float, float), optional) – Interaction strength(s) e.g. 1 or (1., 1., 0.5). Positive is antiferromagnetic.
seed (int, optional) – Random number to seed the noise with.
S ({1/2, 1, 3/2, ...}, optional) – The underlying spin of the system, defaults to 1/2.
cyclic (bool, optional) – Whether to use periodic boundary conditions - default is False.
dh_dist ({'s', 'g', 'qp'}, optional) – Whether to use sqaure, guassian or quasiperiodic noise.
beta (float, optional) – Frequency of the quasirandom noise, only if dh_dist='qr'.
mpo_opts – Supplied to MatrixProductOperator.

Return type:

quimb.tensor.MPO_ham_XY(L, j=1.0, bz=0.0, *, S=1 / 2, cyclic=False, **mpo_opts)[source]¶

XY-Hamiltonian in MPO form.

\[H_\mathrm{XY} = \sum_{i} ( J_X S^X_i S^X_{i + 1} + J_Y S^Y_i S^Y_{i + 1} ) - B_x \sum_{i} S^Z_i\]

Note the default convention of antiferromagnetic interactions and spin operators not Pauli matrices.

Parameters:

L (int) – The number of sites.
j (float or (float, float), optional) – The XX and YY interaction strength. Positive is antiferromagnetic.
bz (float, optional) – The Z-magnetic field strength.
S ({1/2, 1, 3/2, ...}, optional) – The underlying spin of the system, defaults to 1/2.
cyclic (bool, optional) – Generate a MPO with periodic boundary conditions or not, default is open boundary conditions.
local_ham_1d_opts (mpo_opts or) – Supplied to MatrixProductOperator.

Return type:

quimb.tensor.MPO_identity(L, sites=None, phys_dim=2, dtype='float64', cyclic=False, **mpo_opts)[source]¶

Generate an identity MPO of size L.

Parameters:

L (int) – The number of sites.
phys_dim (int, optional) – The physical (site) dimensions, defaults to 2.
dtype ({float, complex} or numpy dtype, optional) – Data type of the tensor network.
cyclic (bool, optional) – Generate a MPO with periodic boundary conditions or not, default is open boundary conditions.
mpo_opts – Supplied to MatrixProductOperator.

quimb.tensor.MPO_identity_like(mpo, **mpo_opts)[source]¶: Return an identity matrix operator with the same physical index and inds/tags as mpo.

quimb.tensor.MPO_product_operator(arrays, cyclic=False, **mpo_opts)[source]¶

Return an MPO of bond dimension 1 representing the product of raw operators given in arrays.

Parameters:

arrays (sequence of 2D array_like) – The operators to form a tensor product of.
cyclic (bool, optional) – Whether to generate a cyclic MPO or not.
mpo_opts – Supplied to MatrixProductOperator.

Return type:

quimb.tensor.MPO_rand(L, bond_dim, phys_dim=2, normalize=True, cyclic=False, herm=False, dtype='float64', dist='normal', loc=0.0, scale=1.0, **mpo_opts)[source]¶

Generate a random matrix product state.

Parameters:

L (int) – The number of sites.
bond_dim (int) – The bond dimension.
phys_dim (int, optional) – The physical (site) dimensions, defaults to 2.
normalize (bool, optional) – Whether to normalize the operator such that trace(A.H @ A) == 1.
cyclic (bool, optional) – Generate a MPO with periodic boundary conditions or not, default is open boundary conditions.
dtype ({float, complex} or numpy dtype, optional) – Data type of the tensor network.
dist ({'normal', 'uniform', 'rademacher', 'exp'}, optional) – Type of random number to generate, defaults to ‘normal’.
loc (float, optional) – An additive offset to add to the random numbers.
scale (float, optional) – A multiplicative factor to scale the random numbers by.
herm (bool, optional) – Whether to make the matrix hermitian (or symmetric if real) or not.
mpo_opts – Supplied to MatrixProductOperator.

quimb.tensor.MPO_rand_herm(L, bond_dim, phys_dim=2, normalize=True, dtype='float64', **mpo_opts)[source]¶: Generate a random hermitian matrix product operator. See MPO_rand.

quimb.tensor.MPO_zeros(L, phys_dim=2, dtype='float64', cyclic=False, **mpo_opts)[source]¶

Generate a zeros MPO of size L.

Parameters:

L (int) – The number of sites.
phys_dim (int, optional) – The physical (site) dimensions, defaults to 2.
dtype ({float, complex} or numpy dtype, optional) – Data type of the tensor network.
cyclic (bool, optional) – Generate a MPO with periodic boundary conditions or not, default is open boundary conditions.
mpo_opts – Supplied to MatrixProductOperator.

Return type:

quimb.tensor.MPO_zeros_like(mpo, **mpo_opts)[source]¶

Return a zeros matrix product operator with the same physical index and inds/tags as mpo.

Parameters:: mpo (MatrixProductOperator) – The MPO to copy the shape of.
Return type:: MatrixProductOperator

quimb.tensor.MPS_computational_state(binary, dtype='float64', cyclic=False, **mps_opts)[source]¶

A computational basis state in Matrix Product State form.

Parameters:

binary (str or sequence of int) – String specifying the state, e.g. '00101010111' or [0, 0, 1].
dtype ({'float64', 'complex128', 'float32', 'complex64'}, optional) – The data type to use for the array representation.
cyclic (bool, optional) – Generate a MPS with periodic boundary conditions or not, default open boundary conditions.
mps_opts – Supplied to MatrixProductState constructor.

quimb.tensor.MPS_ghz_state(L, dtype='float64', **mps_opts)[source]¶

Build the chi=2 OBC MPS representation of the GHZ state.

Parameters:

L (int) – Number of qubits.
dtype ({'float64', 'complex128', 'float32', 'complex64'}, optional) – The underlying data type.
mps_opts – Supplied to MatrixProductState.

quimb.tensor.MPS_neel_state(L, down_first=False, dtype='float64', **mps_opts)[source]¶

Generate the neel state in Matrix Product State form.

Parameters:

L (int) – The number of spins.
down_first (bool, optional) – Whether to start with ‘1’ (down) or ‘0’ (up) first.
mps_opts – Supplied to MatrixProductState constructor.

quimb.tensor.MPS_product_state(arrays, cyclic=False, **mps_opts)[source]¶: Generate a product state in MatrixProductState form, i,e, with bond dimension 1, from single site vectors described by arrays.

quimb.tensor.MPS_rand_computational_state(L, dtype='float64', **mps_opts)[source]¶

Generate a random computation basis state, like ‘01101001010’.

Parameters:

L (int) – The number of qubits.
seed (int, optional) – The seed to use.
dtype ({float, complex} or numpy dtype, optional) – Data type of the tensor network.
mps_opts – Supplied to MatrixProductState.

quimb.tensor.MPS_rand_state(L, bond_dim, phys_dim=2, normalize=True, cyclic=False, dist='normal', loc=0.0, scale=1.0, dtype='float64', trans_invar=False, **mps_opts)[source]¶

Generate a random matrix product state.

Parameters:

L (int) – The number of sites.
bond_dim (int) – The bond dimension.
phys_dim (int, optional) – The physical (site) dimensions, defaults to 2.
normalize (bool, optional) – Whether to normalize the state.
cyclic (bool, optional) – Generate a MPS with periodic boundary conditions or not, default is open boundary conditions.
dist ({'normal', 'uniform', 'rademacher', 'exp'}, optional) – Type of random number to generate, defaults to ‘normal’.
loc (float, optional) – An additive offset to add to the random numbers.
scale (float, optional) – A multiplicative factor to scale the random numbers by.
dtype ({float, complex} or numpy dtype, optional) – Data type of the tensor network.
trans_invar (bool (optional)) – Whether to generate a translationally invariant state, requires cyclic=True.
mps_opts – Supplied to MatrixProductState.

quimb.tensor.MPS_sampler(L, dtype=complex, squeeze=True, **mps_opts)[source]¶: A product state for sampling tensor network traces. Seen as a vector it has the required property that psi.H @ psi == d always for hilbert space size d.

quimb.tensor.MPS_w_state(L, dtype='float64', **mps_opts)[source]¶

Build the chi=2 OBC MPS representation of the W state.

Parameters:

L (int) – Number of qubits.
dtype ({'float64', 'complex128', 'float32', 'complex64'}, optional) – The underlying data type.
mps_opts – Supplied to MatrixProductState.

quimb.tensor.MPS_zero_state(L, bond_dim=1, phys_dim=2, cyclic=False, dtype='float64', **mps_opts)[source]¶

The all-zeros MPS state, of given bond-dimension.

Parameters:

L (int) – The number of sites.
bond_dim (int, optional) – The bond dimension, defaults to 1.
phys_dim (int, optional) – The physical (site) dimensions, defaults to 2.
cyclic (bool, optional) – Generate a MPS with periodic boundary conditions or not, default is open boundary conditions.
dtype ({float, complex} or numpy dtype, optional) – Data type of the tensor network.
mps_opts – Supplied to MatrixProductState.

quimb.tensor.NNI_ham_heis[source]¶

quimb.tensor.NNI_ham_ising[source]¶

quimb.tensor.NNI_ham_mbl[source]¶

quimb.tensor.NNI_ham_XY[source]¶

quimb.tensor.SpinHam[source]¶

class quimb.tensor.SpinHam1D(S=1 / 2, cyclic=False)[source]¶

Class for easily building custom spin hamiltonians in MPO or LocalHam1D form. Currently limited to nearest neighbour interactions (and single site terms). It is possible to set ‘default’ translationally invariant terms, but also terms acting on specific sites only (which take precedence). It is also possible to build a sparse matrix version of the hamiltonian (obviously for small sizes only).

Parameters:

S (float, optional) – The type of spin, defaults to 1/2.
cyclic (bool, optional) – Whether to use periodic boundary conditions - default is False.

Examples

Initialize the spin hamiltonian builder:

>>> builder = SpinHam1D(S=3 / 2)

Add some two-site terms:

>>> builder += 0.5, '+', '-'
>>> builder += 0.5, '-', '+'
>>> builder += 1.0, 'Z', 'Z'

Add a single site term:

>>> builder -= 0.3, 'Z'

Build a MPO version of the hamiltonian for use with DMRG:

>>> mpo_ham = builder.build_mpo(100)
>>> mpo_ham
<MatrixProductOperator(tensors=100, L=100, max_bond=5)>

Build a LocalHam1D version of the hamiltonian for use with TEBD:

>>> builder.build_local_ham(100)
<LocalHam1D(L=100, cyclic=False)>

You can also set terms for specific sites (this overides any of the ‘default’, translationally invariant terms set as above):

>>> builder[10, 11] += 0.75, '+', '-'
>>> builder[10, 11] += 0.75, '-', '+'
>>> builder[10, 11] += 1.5, 'Z', 'Z'

Or specific one-site terms (which again overides any default single site terms set above):

>>> builder[10] += 3.7, 'Z'
>>> builder[11] += 0.0, 'I' # '0' term turns off field

S = 0.5¶

one_site_terms = []¶

two_site_terms = []¶

cyclic = False¶

var_one_site_terms¶

var_two_site_terms¶

add_term(factor, *operators)[source]¶

Add another term to the expression to be built.

Parameters:

factor (scalar) – Scalar factor to multiply this term by.
*operators (str or array) – The operators to use. Can specify one or two for single or two site terms respectively. Can use strings, which are supplied to spin_operator(), or actual arrays as long as they have the correct dimension.

sub_term(factor, *operators)[source]¶: Subtract a term - simple alias that flips sign of factor.

__iadd__(term)[source]¶

__isub__(term)[source]¶

__getitem__(sites)[source]¶

Part of the machinery that allows terms to be added to specific sites like:

>>> builder[i] += 1/2, 'X'
>>> builder[45, 46] += 1/2, 'Z', 'Z'

__setitem__(sites, value)[source]¶

Part of the machinery that allows terms to be added to specific sites like:

>>> builder[i] += 1/2, 'X'
>>> builder[45, 46] += 1/2, 'Z', 'Z'

Could also be called directly with a list of terms like:

>>> builder[13, 14] = [(1, 'Z', 'Z'), (0.5, 'X', 'Y')]

Which would overide any terms set so far.

build_mpo(L, upper_ind_id='k{}', lower_ind_id='b{}', site_tag_id='I{}', tags=None)[source]¶: Build an MPO instance of this spin hamiltonian of size L. See also MatrixProductOperator.

build_sparse(L, **ikron_opts)[source]¶

Build a sparse matrix representation of this Hamiltonian.

Parameters:

L (int, optional) – The number of spins to build the matrix for.
ikron_opts – Supplied to ikron().

Returns:

Return type:

matrix

_get_spin_op(factor, *ss)[source]¶

_sum_spin_ops(terms)[source]¶

build_local_ham(L=None, **local_ham_1d_opts)[source]¶

Build a nearest neighbour interactor instance of this spin hamiltonian of size L. See also LocalHam1D.

Parameters:: L (int, optional) – The number of spins, if the hamiltonian only has two-site terms this is optional.
Return type:: LocalHam1D

quimb.tensor.TN2D_classical_ising_partition_function(Lx, Ly, beta, j=1.0, h=0.0, cyclic=False, site_tag_id='I{},{}', x_tag_id='X{}', y_tag_id='Y{}', outputs=(), ind_id='s{},{}')[source]¶

The tensor network representation of the 2D classical ising model partition function.

Parameters:

Lx (int) – Length of side x.
Ly (int) – Length of side y.
beta (float) – The inverse temperature.
j (float, dict, or callable, optional) – The interaction strength, positive being ferromagnetic. If a dict should contain entries for each edge, keyed by the edge. If a callable should have the signature j(node_a, node_b) and return a float.
h (float, optional) – The magnetic field strength.
cyclic (bool or (bool, bool), optional) – Whether to use periodic boundary conditions. X and Y can be specified separately using a tuple.
site_tag_id (str, optional) – String specifier for naming convention of site tags.
x_tag_id (str, optional) – String specifier for naming convention of row tags.
y_tag_id (str, optional) – String specifier for naming convention of column tags.
outputs (sequence of tuple[int, int], optional) – Which sites to generate output indices (i.e. dangling legs) for. The index is named according to ind_id.
ind_id (str, optional) – How to label the indices i.e. ind_id.format(i, j), each of which corresponds to a single classical spin in outputs.

Return type:

quimb.tensor.TN2D_corner_double_line(Lx, Ly, line_dim=2, tiling=2, fill_missing_edges=True, site_tag_id='I{},{}', x_tag_id='X{}', y_tag_id='Y{}', **kwargs)[source]¶

Build a 2D ‘corner double line’ (CDL) tensor network. Each plaquette contributes a matrix (by default the identity) at each corner, connected in a loop. The corners for each site are then grouped and optionally contracted. Such a tensor network has strong local correlations. See https://arxiv.org/abs/1412.0732. If the sites are not contracted, the resulting network is a product of loops that can be easily and exactly contracted.

Note that if identity matrices are used, the contracted value of the tensor network is line_dim**num_plaquettes.

Parameters:

Lx (int) – Length of side x.
Ly (int) – Length of side y.
line_dim (int, optional) – The dimension of the matrices at each corner. If contract is True, then the resulting bonds with have dimension line_dim**tiling.
tiling ({1, 2}, optional) – How to tile the plaquettes. If 1, the plaquettes are tiled in a checkerboard pattern resulting in a single line per edge. If 2, the plaquettes are tiled in a dense pattern resulting in two lines per edge.
fill_missing_edges (bool, optional) – Whether to fill in the missing edges around the border with open strings, ensuring every bond exists and has the same dimension.
site_tag_id (str, optional) – String specifier for naming convention of site tags.
x_tag_id (str, optional) – String specifier for naming convention of row tags.
y_tag_id (str, optional) – String specifier for naming convention of column tags.
kwargs – Additional keyword arguments are passed to TN_from_strings().

Return type:

See also

TN_from_strings

quimb.tensor.TN2D_embedded_classical_ising_partition_function(Jij, beta, outputs=(), ordering=None, sites_location='side', UPLO='auto', contract_sites=True, site_tag_id='I{},{}', x_tag_id='X{}', y_tag_id='Y{}', ind_id='s{}')[source]¶

Construct a (triangular) ‘2D’ tensor network representation of the classical ising model partition function with all-to-all interactions, by embedding it in a 2D lattice. For N spins this will have N(N-1)/2 tensors, present at sites (i, j) where i > j. If outputs is supplied, then dangling indices will be added for each output, to give a ‘standard’ TN representation of the unnormalized marginal distribution over those outputs. Since each spin is effectively “delocalized” into a COPY-tensor MPS spanning the lattice, there is a choice of where to put the outputs, specified by sites_location.

sites_location="side":

...   .  .  .  ╭──
               │
j=2   .  .  ╭──O──...
            │  │
j=1   .  ╭──O──O──...
         │  │  │
j=0   ╭──O──O──O──...
      │  │  │  │
      s0 s1 s2 s3  <- site indices

    i=0, 1, 2, 3, ...

sites_location="diag":

...   .  .  s3 ╭──
              ╲│
j=2   .  s2 ╭──O──...
           ╲│  │
j=1   s1 ╭──O──O──...
        ╲│  │  │
j=0  s0──O──O──O──...

    i=0, 1, 2, 3, ...

Parameters:

Jij (array_like or dict[(int, int), float]) – The couplings, as a square matrix or explicit dict. If a matrix, the upper or lower triangle (corresponding to UPLO) should the pairwise interaction strengths. If a dict, then each non-zero interaction can be specified explicitly.
beta (float) – The inverse temperature.
outputs (sequence of int, optional) – Which sites to generate output indices (i.e. dangling legs) for. The index is named according to ind_id. The location of the output depends on sites_location.
ordering (sequence of int, optional) – If supplied, the ordering of the embedded spins into the lattice.
sites_location ({"side", "diag"}, optional) – Whether to place the output indices on the side or diagonally. If "side" then the output indices will be placed along the ‘y-min’ border, i.e. [(j, 0) for j in range(1, N)], with two on the first tensor. If "diag" then the output indices will be placed along the central diagonal fold of the lattice, i.e. [(j + 1, j) for j in range(1, N)], with two on the first tensor.
UPLO ({"auto", "L", "U"}, optional) – Whether to read the couplings in from J in lower (‘L’: i >= j) or upper (‘U’: i <= j) form. If J is a dict then this only matters if both are present and in that case acts as a preference. If "auto" then either the lower or upper, or symmetric distances can be supplied, but an error is raised if both are.
contract_sites (bool, optional) – Whether to contract the sites of the TN, which after initial construction will have one horizontol spin-rail, one vertical spin-rail and one interaction tensor per site.
site_tag_id (str, optional) – String formatter specifying how to label each site.
x_tag_id (str, optional) – String formatter specifying how to label each x-plane.
y_tag_id (str, optional) – String formatter specifying how to label each y-plane.
ind_id (str, optional) – How to label the indices i.e. ind_id.format(i), each of which corresponds to a single classical spin in outputs.

Return type:

quimb.tensor.TN2D_empty(Lx, Ly, D, cyclic=False, site_tag_id='I{},{}', x_tag_id='X{}', y_tag_id='Y{}', dtype='float64')[source]¶

A scalar 2D lattice tensor network initialized with empty tensors.

Parameters:

Lx (int) – Length of side x.
Ly (int) – Length of side y.
D (int) – The bond dimension connecting sites.
cyclic (bool or (bool, bool), optional) – Whether to use periodic boundary conditions. X and Y can be specified separately using a tuple.
site_tag_id (str, optional) – String specifier for naming convention of site tags.
x_tag_id (str, optional) – String specifier for naming convention of row tags.
y_tag_id (str, optional) – String specifier for naming convention of column tags.
dtype (str, optional) – The data type of the tensors.

Return type:

quimb.tensor.TN2D_from_fill_fn(fill_fn, Lx, Ly, D, cyclic=False, site_tag_id='I{},{}', x_tag_id='X{}', y_tag_id='Y{}')[source]¶

A scalar 2D lattice tensor network with tensors filled by a function.

Parameters:

fill_fn (callable) – A function with signature fill_fn(shape) -> array, used to fill each tensor.
Lx (int) – Length of side x.
Ly (int) – Length of side y.
D (int) – The bond dimension connecting sites.
cyclic (bool or (bool, bool), optional) – Whether to use periodic boundary conditions. X and Y can be specified separately using a tuple.
site_tag_id (str, optional) – String specifier for naming convention of site tags.
x_tag_id (str, optional) – String specifier for naming convention of row tags.
y_tag_id (str, optional) – String specifier for naming convention of column tags.

Return type:

quimb.tensor.TN2D_rand(Lx, Ly, D, cyclic=False, site_tag_id='I{},{}', x_tag_id='X{}', y_tag_id='Y{}', dist='normal', loc=0, scale=1, seed=None, dtype='float64')[source]¶

A random scalar 2D lattice tensor network.

Parameters:

Lx (int) – Length of side x.
Ly (int) – Length of side y.
D (int) – The bond dimension connecting sites.
cyclic (bool or (bool, bool), optional) – Whether to use periodic boundary conditions. X and Y can be specified separately using a tuple.
site_tag_id (str, optional) – String specifier for naming convention of site tags.
x_tag_id (str, optional) – String specifier for naming convention of row tags.
y_tag_id (str, optional) – String specifier for naming convention of column tags.
dist ({'normal', 'uniform', 'rademacher', 'exp'}, optional) – Type of random number to generate, defaults to ‘normal’.
loc (float, optional) – An additive offset to add to the random numbers.
scale (float, optional) – A multiplicative factor to scale the random numbers by.
seed (int, optional) – A random seed.
dtype (dtype, optional) – Data type of the random arrays.

Return type:

quimb.tensor.TN2D_rand_hidden_loop(Lx, Ly, *, cyclic=False, line_dim=2, line_density=2, seed=None, dist='normal', dtype='float64', loc=0.0, scale=1.0, gauge_random=True, site_tag_id='I{},{}', x_tag_id='X{}', y_tag_id='Y{}', **kwargs)[source]¶

quimb.tensor.TN2D_rand_symmetric(Lx, Ly, D, cyclic=False, site_tag_id='I{},{}', x_tag_id='X{}', y_tag_id='Y{}', dist='normal', loc=0, scale=1, seed=None, dtype='float64')[source]¶

Create a random 2D lattice tensor network where every tensor is symmetric up to index permutations.

Parameters:

Lx (int) – Length of side x.
Ly (int) – Length of side y.
D (int) – The bond dimension connecting sites.
cyclic (bool or (bool, bool), optional) – Whether to use periodic boundary conditions. X and Y can be specified separately using a tuple.
site_tag_id (str, optional) – String specifier for naming convention of site tags.
x_tag_id (str, optional) – String specifier for naming convention of row tags.
y_tag_id (str, optional) – String specifier for naming convention of column tags.
dist ({'normal', 'uniform', 'rademacher', 'exp'}, optional) – Type of random number to generate, defaults to ‘normal’.
loc (float, optional) – An additive offset to add to the random numbers.
scale (float, optional) – A multiplicative factor to scale the random numbers by.
seed (int, optional) – A random seed.
dtype (dtype, optional) – Data type of the random arrays.

Return type:

quimb.tensor.TN2D_with_value(value, Lx, Ly, D, cyclic=False, site_tag_id='I{},{}', x_tag_id='X{}', y_tag_id='Y{}', dtype=None)[source]¶

A scalar 2D lattice tensor network with every element set to value. This uses numpy.broadcast_to and therefore essentially no memory.

Parameters:

value (scalar) – The value to fill the tensors with.
Lx (int) – Length of side x.
Ly (int) – Length of side y.
D (int) – The bond dimension connecting sites.
cyclic (bool or (bool, bool), optional) – Whether to use periodic boundary conditions. X and Y can be specified separately using a tuple.
site_tag_id (str, optional) – String specifier for naming convention of site tags.
x_tag_id (str, optional) – String specifier for naming convention of row tags.
y_tag_id (str, optional) – String specifier for naming convention of column tags.
dtype (str, optional) – The data type of the tensors.

Return type:

quimb.tensor.TN3D_classical_ising_partition_function(Lx, Ly, Lz, beta, j=1.0, h=0.0, cyclic=False, site_tag_id='I{},{},{}', x_tag_id='X{}', y_tag_id='Y{}', z_tag_id='Z{}', outputs=(), ind_id='s{},{},{}')[source]¶

Tensor network representation of the 3D classical ising model partition function.

Parameters:

Lx (int) – Length of side x.
Ly (int) – Length of side y.
Lz (int) – Length of side z.
beta (float) – The inverse temperature.
j (float, dict, or callable, optional) – The interaction strength, positive being ferromagnetic. If a dict should contain entries for each edge, keyed by the edge. If a callable should have the signature j(node_a, node_b) and return a float.
h (float, optional) – The magnetic field strength.
cyclic (bool or (bool, bool, bool), optional) – Whether to use periodic boundary conditions. X, Y and Z can be specified separately using a tuple.
site_tag_id (str, optional) – String formatter specifying how to label each site.
x_tag_id (str, optional) – String formatter specifying how to label each x-plane.
y_tag_id (str, optional) – String formatter specifying how to label each y-plane.
z_tag_id (str, optional) – String formatter specifying how to label each z-plane.
outputs (sequence of tuple[int, int, int], optional) – Which sites to generate output indices (i.e. dangling legs) for. The index is named according to ind_id.
ind_id (str, optional) – How to label the indices i.e. ind_id.format(i, j, k), each of which corresponds to a single classical spin in outputs.

Return type:

quimb.tensor.TN3D_corner_double_line(Lx, Ly, Lz, line_dim=2, tiling=2, fill_missing_edges=True, site_tag_id='I{},{},{}', x_tag_id='X{}', y_tag_id='Y{}', z_tag_id='Z{}', **kwargs)[source]¶

quimb.tensor.TN3D_empty(Lx, Ly, Lz, D, cyclic=False, site_tag_id='I{},{},{}', x_tag_id='X{}', y_tag_id='Y{}', z_tag_id='Z{}', dtype='float64')[source]¶

A scalar 3D lattice tensor network initialized with empty tensors.

Parameters:

Lx (int) – Length of side x.
Ly (int) – Length of side y.
Lz (int) – Length of side z.
D (int) – The bond dimension connecting sites.
cyclic (bool or (bool, bool, bool), optional) – Whether to use periodic boundary conditions. X, Y and Z can be specified separately using a tuple.
site_tag_id (str, optional) – String formatter specifying how to label each site.
dtype (dtype, optional) – Data type of the random arrays.
seed (int, optional) – Random seed.

Return type:

quimb.tensor.TN3D_from_fill_fn(fill_fn, Lx, Ly, Lz, D, cyclic=False, site_tag_id='I{},{},{}', x_tag_id='X{}', y_tag_id='Y{}', z_tag_id='Z{}')[source]¶

A scalar 3D lattice tensor network with tensors filled by a function.

Parameters:

fill_fn (callable) – A function with signature fill_fn(shape) -> array, used to fill each tensor.
Lx (int) – Length of side x.
Ly (int) – Length of side y.
Lz (int) – Length of side z.
D (int) – The bond dimension connecting sites.
cyclic (bool or (bool, bool, bool), optional) – Whether to use periodic boundary conditions. X, Y and Z can be specified separately using a tuple.
site_tag_id (str, optional) – String formatter specifying how to label each site.
dtype (dtype, optional) – Data type of the random arrays.

Return type:

quimb.tensor.TN3D_rand(Lx, Ly, Lz, D, cyclic=False, site_tag_id='I{},{},{}', x_tag_id='X{}', y_tag_id='Y{}', z_tag_id='Z{}', dist='normal', loc=0.0, scale=1.0, seed=None, dtype='float64')[source]¶

A random scalar 3D lattice tensor network.

Parameters:

Lx (int) – Length of side x.
Ly (int) – Length of side y.
Lz (int) – Length of side z.
D (int) – The bond dimension connecting sites.
cyclic (bool or (bool, bool, bool), optional) – Whether to use periodic boundary conditions. X, Y and Z can be specified separately using a tuple.
site_tag_id (str, optional) – String formatter specifying how to label each site.
dtype (dtype, optional) – Data type of the random arrays.
seed (int, optional) – Random seed.

Return type:

quimb.tensor.TN3D_rand_hidden_loop(Lx, Ly, Lz, *, cyclic=False, line_dim=2, line_density=2, seed=None, dist='normal', dtype='float64', loc=0.0, scale=1.0, gauge_random=True, site_tag_id='I{},{},{}', x_tag_id='X{}', y_tag_id='Y{}', z_tag_id='Z{}', **kwargs)[source]¶

quimb.tensor.TN3D_with_value(value, Lx, Ly, Lz, D, cyclic=False, site_tag_id='I{},{},{}', x_tag_id='X{}', y_tag_id='Y{}', z_tag_id='Z{}', dtype=None)[source]¶

A scalar 2D lattice tensor network with every element set to value. This uses numpy.broadcast_to and therefore essentially no memory.

Parameters:

value (scalar) – The value to fill the tensors with.
Lx (int) – Length of side x.
Ly (int) – Length of side y.
Lz (int) – Length of side z.
D (int) – The bond dimension connecting sites.
cyclic (bool or (bool, bool, bool), optional) – Whether to use periodic boundary conditions. X, Y and Z can be specified separately using a tuple.
site_tag_id (str, optional) – String formatter specifying how to label each site.
dtype (dtype, optional) – Data type of the random arrays.
seed (int, optional) – Random seed.

Return type:

quimb.tensor.TN_classical_partition_function_from_edges(edges, beta, j=1.0, h=0.0, site_tag_id='I{}', bond_ind_id='b{},{}', outputs=(), ind_id='s{}')[source]¶

Build a regular tensor network representation of a classical ising model partition function by specifying graph edges. There will be a single tensor per site.

Parameters:

edges (sequence of tuple[hashable, hashable]) – The graph edges, as a sequence of pairs of hashable objects, for example integers, representing the nodes. You can redundantly specify (u, v) and (v, u) and only one edge will be added.
beta (float, optional) – The inverse temperature.
j (float, dict, or callable, optional) – The interaction strength, positive being ferromagnetic. If a dict should contain entries for each edge, keyed by the edge. If a callable should have the signature j(node_a, node_b) and return a float.
h (float, or callable, optional) – The magnetic field strength. If a callable should have the signature h(node) and return a float.
site_tag_id (str, optional) – A string formatter for naming tensor tags like site_ind_id.format(node).
bond_ind_id (str, optional) – A string formatter for naming the indices bewteen tensors like bond_ind_id.format(node_a, node_b).

Return type:

quimb.tensor.TN_dimer_covering_from_edges(edges, cover_count=1, site_tag_id='I{}', bond_ind_id='b{},{}', dtype=float)[source]¶

Make a tensor network from sequence of graph edges that counts the number of ways to cover the graph exactly with dimers. See https://arxiv.org/abs/1805.10598 for the construction.

Parameters:

edges (sequence of tuple) – The edges, each item should be a pair of hashable objects describing nodes linked.
cover_count (int, optional) – The exact number of times each node must be ‘covered’. For example 1 for a standard dimer covering or 2 for ‘ice rules’.
site_tag_id (str, optional) – A string formatter for naming tensor tags like site_ind_id.format(node).
bond_ind_id (str, optional) – A string formatter for naming the indices bewteen tensors like bond_ind_id.format(node_a, node_b).

Return type:

TensorNetworkGen, TensorNetworkGenVector or TensorNetworkGenOperator

quimb.tensor.TN_from_edges_and_fill_fn(fill_fn, edges, D, phys_dim=None, site_tag_id='I{}', site_ind_id='k{}')[source]¶

Create a tensor network from a sequence of edges defining a graph, and a ‘fill’ function that maps shapes to data.

Parameters:

fill_fn (callable) – A function with signature fill_fn(shape) -> array, used to fill each tensor.
edges (sequence of tuple[hashable, hashable]) – The graph edges, as a sequence of pairs of hashable objects, for example integers, representing the nodes. You can redundantly specify (u, v) and (v, u) and only one edge will be added.
D (int) – The bond dimension connecting tensors.
phys_dim (int, optional) – If not None, give each tensor a ‘physical’, free index of this size at each node.
site_tag_id (str, optional) – String with formatter to tag sites.
site_ind_id (str or (str, str), optional) – String with formatter to tag indices (if phys_dim specified). If a single str is supplied, the tensor network will have a single index at each site, representing a vector. If a pair of strings is supplied, the tensor network will have two indices at each site, representing an operator with upper and lower indices.

Return type:

quimb.tensor.TN_from_edges_empty(edges, D, phys_dim=None, site_tag_id='I{}', site_ind_id='k{}', dtype='float64')[source]¶

Create a tensor network from a sequence of edges defining a graph, initialized with empty tensors.

Parameters:

edges (sequence of tuple[hashable, hashable]) – The graph edges, as a sequence of pairs of hashable objects, for example integers, representing the nodes. You can redundantly specify (u, v) and (v, u) and only one edge will be added.
D (int) – The bond dimension connecting tensors.
phys_dim (int, optional) – If not None, give each tensor a ‘physical’, free index of this size at each node.
site_tag_id (str, optional) – String with formatter to tag sites.
site_ind_id (str, optional) – String with formatter to tag indices (if phys_dim specified).
dtype (str, optional) – The data type of the tensors.

Return type:

TensorNetworkGen, TensorNetworkGenVector or TensorNetworkGenOperator

quimb.tensor.TN_from_edges_rand(edges, D, phys_dim=None, seed=None, dtype='float64', site_tag_id='I{}', site_ind_id='k{}', **randn_opts)[source]¶

Create a random tensor network with geometry defined from a sequence of edges defining a graph.

Parameters:

G (sequence of tuple[node, node]) – The edges defining a graph, each element should be a pair of nodes described by hashable objects.
D (int) – The bond dimension connecting tensors.
phys_dim (int, optional) – If not None, give each tensor a ‘physical’, free index of this size to mimic a wavefunction of len(G) sites.
seed (int, optional) – A random seed.
dtype (str, optional) – The data type of the tensors.
site_tag_id (str, optional) – String with formatter to tag sites.
site_ind_id (str, optional) – String with formatter to tag indices (if phys_dim specified).
randn_opts – Supplied to randn().

Return type:

TensorNetworkGen, TensorNetworkGenVector or TensorNetworkGenOperator

quimb.tensor.TN_from_edges_with_value(value, edges, D, phys_dim=None, site_tag_id='I{}', site_ind_id='k{}', dtype=None)[source]¶

Create a tensor network from a sequence of edges defining a graph, initialized with a constant value. This uses numpy.broadcast_to and therefore essentially no memory.

Parameters:

value (scalar) – The value to fill the tensors with.
edges (sequence of tuple[hashable, hashable]) – The graph edges, as a sequence of pairs of hashable objects, for example integers, representing the nodes. You can redundantly specify (u, v) and (v, u) and only one edge will be added.
D (int) – The bond dimension connecting tensors.
phys_dim (int, optional) – If not None, give each tensor a ‘physical’, free index of this size at each node.
site_tag_id (str, optional) – String with formatter to tag sites.
site_ind_id (str, optional) – String with formatter to tag indices (if phys_dim specified).
dtype (str, optional) – The data type of the tensors.

Return type:

TensorNetworkGen, TensorNetworkGenVector or TensorNetworkGenOperator

quimb.tensor.TN_from_sites_computational_state(site_map, site_tag_id='I{}', site_ind_id='k{}', dtype='float64')[source]¶

A computational basis state in general tensor network form.

Parameters:

site_map (dict[hashable, str]) – Mapping of site to computational state, which should be one of ('0', '1', '+', '-').
site_tag_id (str, optional) – Format string for site tag labels.
site_ind_id (str, optional) – Format string for site index labels.
dtype ({'float64', 'complex128', 'float32', 'complex64'}, optional) – The data type to use for the array representation.

Return type:

quimb.tensor.TN_from_sites_product_state(site_map, site_tag_id='I{}', site_ind_id='k{}')[source]¶

A product state in general tensor network form.

Parameters:

site_map (dict[hashable, array_like]) – Mapping of site to local state.
site_tag_id (str, optional) – Format string for site tag labels.
site_ind_id (str, optional) – Format string for site index labels.

Return type:

quimb.tensor.TN_from_strings(strings, fill_fn=None, line_dim=2, allow_plaquettes=True, site_tag_id='I{}', random_rewire=False, random_rewire_seed=None, join=False, join_avoid_self_loops=True, normalize=False, contract_sites=True, fuse_multibonds=True, **contract_opts)[source]¶

quimb.tensor.TN_matching(tn, max_bond, site_tags=None, fill_fn=None, dtype=None, **randn_opts)[source]¶

Create a tensor network with the same outer indices as tn but with a single tensor per site with bond dimension max_bond between each connected site. Generally to be used as an initial guess for fitting.

Parameters:

tn (TensorNetwork) – The tensor network to match, it can have arbitrary local structure and output indices, as long as site_tags effectively partitions it.
max_bond (int) – The bond dimension to use between each site.
site_tags (sequence of str, optional) – The tags to use to select the tensors from tn. If not given, uses tn.site_tags. The tensor network built will have one tensor per site, in the order given by site_tags.
dtype (dtype, optional) – The data type to use for the new tensors, if not given uses the same as the original tensors.
randn_opts – Supplied to randn().

Return type:

TensorNetworkGen, TensorNetworkGenVector or TensorNetworkGenOperator

quimb.tensor.TN_rand_from_edges[source]¶

quimb.tensor.TN_rand_reg(n, reg, D, phys_dim=None, seed=None, dtype='float64', site_tag_id='I{}', site_ind_id='k{}', **randn_opts)[source]¶

Create a random regular tensor network.

Parameters:

n (int) – The number of tensors.
reg (int) – The degree of the tensor network (how many tensors each tensor connects to).
D (int) – The bond dimension connecting tensors.
phys_dim (int, optional) – If not None, give each tensor a ‘physical’, free index of this size to mimic a wavefunction of n sites.
seed (int, optional) – A random seed.
site_tag_id (str, optional) – String with formatter to tag sites.
site_ind_id (str, optional) – String with formatter to tag indices (if phys_dim specified).

Return type:

quimb.tensor.TN_rand_tree(n, D, phys_dim=None, max_degree=None, seed=None, dtype='float64', site_tag_id='I{}', site_ind_id='k{}', **randn_opts)[source]¶

Create a random tree tensor network.

Parameters:

n (int) – The number of tensors.
D (int) – The bond dimension connecting tensors.
phys_dim (int, optional) – If not None, give each tensor a ‘physical’, free index of this size to mimic a wavefunction of n sites.
max_degree (int, optional) – The maximum degree of any node in the tree, for example 3 means that each tensor can connect to at most 3 other tensors - creating a binary tree (1 parent and two children).
seed (int, optional) – A random seed.
site_tag_id (str, optional) – String with formatter to tag sites.
site_ind_id (str, optional) – String with formatter to tag indices (if phys_dim specified).

Return type:

TensorNetworkGen, TensorNetworkGenVector or TensorNetworkGenOperator

quimb.tensor.cnf_file_parse(fname)[source]¶

Parse a DIMACS style ‘cnf’ file into a list of clauses, and possibly a dictionary of weights. The weights, if present, can be specified either as:

(CACHET format): a line per weight like w {signed_var} {weight}, where signed_var is an integer whose sign specifies the sign of the weight being set.

(MC2021 competition format): the same as above, but with each line specified as c p weight {signed_var} {weight}.

(MINIC2D format): a single line of the form c weights {wp_1} {wm_1} {wp_2} {wm_2}... ``, where ``wp_i and wn_i are the positive and negative weights for variable i. Weights specified this way are overriden by the previous two formats.

Parameters:: fname (str) – Path to a ‘.cnf’ or ‘.wcnf’ file.
Returns:: instance
Return type:: dict

quimb.tensor.convert_to_2d(tn, Lx=None, Ly=None, site_tag_id='I{},{}', x_tag_id='X{}', y_tag_id='Y{}', inplace=False)[source]¶: Convert tn to a TensorNetwork2D, assuming that is has a generic geometry with sites labelled by (i, j) coordinates already. Useful for constructing 2D tensor networks from functions that only require a list of edges etc.

quimb.tensor.convert_to_3d(tn, Lx=None, Ly=None, Lz=None, site_tag_id='I{},{},{}', x_tag_id='X{}', y_tag_id='Y{}', z_tag_id='Z{}', inplace=False)[source]¶: Convert tn to a TensorNetwork3D, assuming that is has a generic geometry with sites labelled by (i, j, k) coordinates already. Useful for constructing 3D tensor networks from functions that only require a list of edges etc.

quimb.tensor.ham_1d_heis(L=None, j=1.0, bz=0.0, *, S=1 / 2, cyclic=False, **local_ham_1d_opts)[source]¶

Heisenberg Hamiltonian in LocalHam1D form.

\[H_\mathrm{Heis} = \sum_{i} ( J_X S^X_i S^X_{i + 1} + J_Y S^Y_i S^Y_{i + 1} + J_Z S^Z_i S^Z_{i + 1} ) - B_Z \sum_{i} S^Z_i\]

Note the default convention of antiferromagnetic interactions and spin operators not Pauli matrices.

Parameters:

L (int) – The number of sites.
j (float or (float, float, float), optional) – The XX, YY and ZZ interaction strength. Positive is antiferromagnetic.
bz (float, optional) – The Z-magnetic field strength.
S ({1/2, 1, 3/2, ...}, optional) – The underlying spin of the system, defaults to 1/2.
cyclic (bool, optional) – Generate a hamiltonian with periodic boundary conditions or not, default is open boundary conditions.
local_ham_1d_opts – Supplied to LocalHam1D.

Return type:

quimb.tensor.ham_1d_ising(L=None, j=1.0, bx=0.0, *, S=1 / 2, cyclic=False, **local_ham_1d_opts)[source]¶

Ising Hamiltonian in LocalHam1D form.

\[H_\mathrm{Ising} = J \sum_{i} S^Z_i S^Z_{i + 1} - B_x \sum_{i} S^X_i\]

Note the default convention of antiferromagnetic interactions and spin operators not Pauli matrices.

Parameters:

L (int) – The number of sites.
j (float, optional) – The ZZ interaction strength. Positive is antiferromagnetic.
bx (float, optional) – The X-magnetic field strength.
S ({1/2, 1, 3/2, ...}, optional) – The underlying spin of the system, defaults to 1/2.
cyclic (bool, optional) – Generate a hamiltonian with periodic boundary conditions or not, default is open boundary conditions.
local_ham_1d_opts (mpo_opts or) – Supplied to LocalHam1D.

Return type:

quimb.tensor.ham_1d_mbl(L, dh, j=1.0, seed=None, S=1 / 2, *, cyclic=False, dh_dist='s', dh_dim=1, beta=None, **local_ham_1d_opts)[source]¶

The many-body-localized spin hamiltonian in LocalHam1D form.

\[H_\mathrm{MBL} = \sum_{i} ( J_X S^X_i S^X_{i + 1} + J_Y S^Y_i S^Y_{i + 1} + J_Z S^Z_i S^Z_{i + 1} ) - \sum_{i} h_i S^Z_i\]

Note the default convention of antiferromagnetic interactions and spin operators not Pauli matrices.

Parameters:

L (int) – Number of spins.
dh (float) – Random noise strength.
j (float, or (float, float, float), optional) – Interaction strength(s) e.g. 1 or (1., 1., 0.5). Positive is antiferromagnetic.
seed (int, optional) – Random number to seed the noise with.
S ({1/2, 1, 3/2, ...}, optional) – The underlying spin of the system, defaults to 1/2.
cyclic (bool, optional) – Whether to use periodic boundary conditions - default is False.
dh_dist ({'s', 'g', 'qp'}, optional) – Whether to use sqaure, guassian or quasiperiodic noise.
beta (float, optional) – Frequency of the quasirandom noise, only if dh_dist='qr'.
local_ham_1d_opts – Supplied to LocalHam1D.

Return type:

quimb.tensor.ham_1d_XY(L=None, j=1.0, bz=0.0, *, S=1 / 2, cyclic=False, **local_ham_1d_opts)[source]¶

XY-Hamiltonian in LocalHam1D form.

\[H_\mathrm{XY} = \sum_{i} ( J_X S^X_i S^X_{i + 1} + J_Y S^Y_i S^Y_{i + 1} ) - B_Z \sum_{i} S^Z_i\]

Note the default convention of antiferromagnetic interactions and spin operators not Pauli matrices.

Parameters:

L (int) – The number of sites.
j (float or (float, float), optional) – The XX and YY interaction strength. Positive is antiferromagnetic.
bz (float, optional) – The Z-magnetic field strength.
S ({1/2, 1, 3/2, ...}, optional) – The underlying spin of the system, defaults to 1/2.
cyclic (bool, optional) – Generate a hamiltonian with periodic boundary conditions or not, default is open boundary conditions.
local_ham_1d_opts – Supplied to LocalHam1D.

Return type:

quimb.tensor.ham_2d_heis(Lx, Ly, j=1.0, bz=0.0, **local_ham_2d_opts)[source]¶

Heisenberg Hamiltonian in LocalHam2D. form.

\[H_\mathrm{Heis} = \sum_{<ij>} ( J_X S^X_i S^X_{j} + J_Y S^Y_i S^Y_{j} + J_Z S^Z_i S^Z_{j} ) - B_Z \sum_{i} S^Z_{i}\]

for nearest neighbors \(<ij>\). Note the default convention of antiferromagnetic interactions and spin operators not Pauli matrices.

Parameters:

Lx (int) – The number of rows.
Ly (int) – The number of columns.
j (float or (float, float, float), optional) – The XX, YY and ZZ interaction strength. Positive is antiferromagnetic.
bz (float, optional) – The Z-magnetic field strength.
local_ham_2d_opts – Supplied to LocalHam2D.

Return type:

LocalHam2D

quimb.tensor.ham_2d_ising(Lx, Ly, j=1.0, bx=0.0, **local_ham_2d_opts)[source]¶

Ising Hamiltonian in LocalHam2D form.

\[H_\mathrm{Ising} = J \sum_{<ij>} S^Z_i S^Z_{j} - B_x \sum_{i} S^X_i\]

for nearest neighbors \(<ij>\). Note the default convention of antiferromagnetic interactions and spin operators not Pauli matrices.

Parameters:

Lx (int) – The number of rows.
Ly (int) – The number of columns.
j (float, optional) – The ZZ interaction strength. Positive is antiferromagnetic.
bx (float, optional) – The X-magnetic field strength.
local_ham_2d_opts – Supplied to LocalHam2D.

Return type:

LocalHam2D

quimb.tensor.ham_2d_j1j2(Lx, Ly, j1=1.0, j2=0.5, bz=0.0, **local_ham_2d_opts)[source]¶

Heisenberg Hamiltonian in LocalHam2D. form.

\[H_\mathrm{Heis} = \sum_{<ij>} ( J_{1,X} S^X_i S^X_{j} + J_{1,Y} S^Y_i S^Y_{j} + J_{1,Z} S^Z_i S^Z_{j} ) + \sum_{<<ij>>} ( J_{2,X} S^X_i S^X_{j} + J_{2,Y} S^Y_i S^Y_{j} + J_{2,Z} S^Z_i S^Z_{j} ) - B_Z \sum_{i} S^Z_{i}\]

for nearest neighbors \(<ij>\) and diagonal next nearest neighbors \(<<ij>>\). Note the default convention of antiferromagnetic interactions and spin operators not Pauli matrices.

Parameters:

Lx (int) – The number of rows.
Ly (int) – The number of columns.
j2 (float or (float, float, float), optional) – The nearest neighbor XX, YY and ZZ interaction strength. Positive is antiferromagnetic.
j2 – The diagonal next nearest nearest XX, YY and ZZ interaction strength. Positive is antiferromagnetic.
bz (float, optional) – The Z-magnetic field strength.
local_ham_2d_opts – Supplied to LocalHam2D.

Return type:

LocalHam2D

quimb.tensor.ham_3d_heis(Lx, Ly, Lz, j=1.0, bz=0.0, **local_ham_3d_opts)[source]¶

Heisenberg Hamiltonian in LocalHam3D. form.

\[H_\mathrm{Heis} = \sum_{<ij>} ( J_X S^X_i S^X_{j} + J_Y S^Y_i S^Y_{j} + J_Z S^Z_i S^Z_{j} ) - B_Z \sum_{i} S^Z_{i}\]

for nearest neighbors \(<ij>\). Note the default convention of antiferromagnetic interactions and spin operators not Pauli matrices.

Parameters:

Lx (int) – The number of x-planes.
Ly (int) – The number of y-planes.
Ly – The number of z-planes.
j (float or (float, float, float), optional) – The XX, YY and ZZ interaction strength. Positive is antiferromagnetic.
bz (float, optional) – The Z-magnetic field strength.
local_ham_3d_opts – Supplied to LocalHam3D.

Return type:

LocalHam3D

quimb.tensor.rand_phased(shape, inds, tags=None, dtype=complex)[source]¶

Generate a random tensor with specified shape and inds, and randomly ‘phased’ (distributed on the unit circle) data, such that T.H @ T == T.norm()**2 == T.size.

Parameters:

shape (sequence of int) – Size of each dimension.
inds (sequence of str) – Names of each dimension.
tags (sequence of str) – Labels to tag this tensor with.
dtype ({'complex128', 'complex64'}, optional) – The underlying data type - can only be complex.

Return type:

quimb.tensor.rand_tensor(shape, inds, tags=None, dtype='float64', dist='normal', scale=1.0, loc=0.0, left_inds=None, **randn_opts)[source]¶

Generate a random tensor with specified shape and inds.

Parameters:

shape (sequence of int) – Size of each dimension.
inds (sequence of str) – Names of each dimension.
tags (sequence of str) – Labels to tag this tensor with.
dtype ({'float64', 'complex128', 'float32', 'complex64'}, optional) – The underlying data type.
dist ({'normal', 'uniform', 'rademacher', 'exp'}, optional) – Type of random number to generate, defaults to ‘normal’.
scale (float, optional) – A multiplicative factor to scale the random numbers by.
loc (float, optional) – An additive offset to add to the random numbers.
left_inds (sequence of str, optional) – Which, if any, indices to group as ‘left’ indices of an effective matrix. This can be useful, for example, when automatically applying unitary constraints to impose a certain flow on a tensor network but at the atomistic (Tensor) level.

Return type:

quimb.tensor.random_ksat_instance(k, num_variables, num_clauses=None, alpha=None, seed=None, allow_repeat_variables=False)[source]¶

Create a random k-SAT instance.

Parameters:

k (int) – Number of variables per clause.
num_variables (int) – Number of variables in the instance.
num_clauses (int, optional) – Number of clauses in the instance. If not specified, will be determined from alpha.
alpha (float, optional) – If num_clauses is not directly specified then the average number of clauses per variable. Taken as a Poisson parameter. Either this or num_clauses must be specified.
seed (int, optional) – Random seed.
allow_repeat_variables (bool, optional) – Whether to allow the same variable to appear multiple times in a single clause.

Returns:

instance – Dictionary with keys ‘num_variables’, ‘num_clauses’, ‘clauses’. The ‘clauses’ key contains a list of tuples, each tuple representing a clause. Within each tuple, each element is an integer representing a variable, with the sign of the integer representing the sign of the variable in the clause.

Return type:

dict

quimb.tensor.COPY_tensor(d, inds, tags=None, dtype=float)[source]¶

Get the tensor representing the COPY operation with dimension size d and number of dimensions len(inds), with exterior indices inds.

Parameters:

d (int) – The size of each dimension.
inds (sequence of str) – The exterior index names for each dimension.
tags (None or sequence of str, optional) – Tag the tensor with these.
dtype (str, optional) – Data type to create the underlying numpy array with.

Returns:

The tensor describing the MPS, of size d**len(inds).

Return type:

class quimb.tensor.IsoTensor(data=1.0, inds=(), tags=None, left_inds=None)[source]¶

Bases: Tensor

A Tensor subclass which keeps its left_inds by default even when its data is changed.

__slots__ = ('_data', '_inds', '_tags', '_left_inds', '_owners')¶

modify(**kwargs)[source]¶

Overwrite the data of this tensor in place.

Parameters:

data (array, optional) – New data.
apply (callable, optional) – A function to apply to the current data. If data is also given this is applied subsequently.
inds (sequence of str, optional) – New tuple of indices.
tags (sequence of str, optional) – New tags.
left_inds (sequence of str, optional) – New grouping of indices to be ‘on the left’.

fuse(*args, inplace=False, **kwargs)[source]¶

Combine groups of indices into single indices.

Parameters:: fuse_map (dict_like or sequence of tuples.) – Mapping like: {new_ind: sequence of existing inds, ...} or an ordered mapping like [(new_ind_1, old_inds_1), ...] in which case the output tensor’s fused inds will be ordered. In both cases the new indices are created at the minimum axis of any of the indices that will be fused.
Returns:: The transposed, reshaped and re-labeled tensor.
Return type:: Tensor

class quimb.tensor.PTensor(fn, params, inds=(), tags=None, left_inds=None)[source]¶

Bases: Tensor

A tensor whose data array is lazily generated from a set of parameters and a function.

Parameters:

fn (callable) – The function that generates the tensor data from params.
params (sequence of numbers) – The initial parameters supplied to the generating function like fn(params).
inds (optional) – Should match the shape of fn(params), see Tensor.
tags (optional) – See Tensor.
left_inds (optional) – See Tensor.

See also

PTensor

__slots__ = ('_data', '_inds', '_tags', '_left_inds', '_owners')¶

classmethod from_parray(parray, inds=(), tags=None, left_inds=None)[source]¶

copy()[source]¶: Copy this parametrized tensor.

_set_data(x)[source]¶

property data¶

property fn¶

get_params()[source]¶: Get the parameters of this PTensor.

set_params(params)[source]¶: Set the parameters of this PTensor.

property params¶

property shape¶: The size of each dimension.

property backend¶: The backend inferred from the data.

_apply_function(fn)[source]¶: Apply fn to the data array of this PTensor (lazily), by composing it with the current parametrized array function.

conj(inplace=False)[source]¶: Conjugate this parametrized tensor - done lazily whenever the .data attribute is accessed.

conj_[source]¶

unparametrize()[source]¶: Turn this PTensor into a normal Tensor.

__getstate__()[source]¶

__setstate__(state)[source]¶

class quimb.tensor.Tensor(data=1.0, inds=(), tags=None, left_inds=None)[source]¶

A labelled, tagged n-dimensional array. The index labels are used instead of axis numbers to identify dimensions, and are preserved through operations. The tags are used to identify the tensor within networks, and are combined when tensors are contracted together.

Parameters:

data (numpy.ndarray) – The n-dimensional data.
inds (sequence of str) – The index labels for each dimension. Must match the number of dimensions of data.
tags (sequence of str, optional) – Tags with which to identify and group this tensor. These will be converted into a oset.
left_inds (sequence of str, optional) – Which, if any, indices to group as ‘left’ indices of an effective matrix. This can be useful, for example, when automatically applying unitary constraints to impose a certain flow on a tensor network but at the atomistic (Tensor) level.

Examples

Basic construction:

>>> from quimb import randn
>>> from quimb.tensor import Tensor
>>> X = Tensor(randn((2, 3, 4)), inds=['a', 'b', 'c'], tags={'X'})
>>> Y = Tensor(randn((3, 4, 5)), inds=['b', 'c', 'd'], tags={'Y'})

Indices are automatically aligned, and tags combined, when contracting:

>>> X @ Y
Tensor(shape=(2, 5), inds=('a', 'd'), tags={'Y', 'X'})

__slots__ = ('_data', '_inds', '_tags', '_left_inds', '_owners')¶

_owners¶

_set_data(data)[source]¶

_set_inds(inds)[source]¶

_set_tags(tags)[source]¶

_set_left_inds(left_inds)[source]¶

get_params()[source]¶: A simple function that returns the ‘parameters’ of the underlying data array. This is mainly for providing an interface for ‘structured’ arrays e.g. with block sparsity to interact with optimization.

set_params(params)[source]¶: A simple function that sets the ‘parameters’ of the underlying data array. This is mainly for providing an interface for ‘structured’ arrays e.g. with block sparsity to interact with optimization.

copy(deep=False, virtual=False)[source]¶

Copy this tensor.

Note

By default (deep=False), the underlying array will not be copied.

Parameters:

deep (bool, optional) – Whether to copy the underlying data as well.
virtual (bool, optional) – To conveniently mimic the behaviour of taking a virtual copy of tensor network, this simply returns self.

__copy__[source]¶

property data¶

property inds¶

property tags¶

property left_inds¶

check()[source]¶: Do some basic diagnostics on this tensor, raising errors if something is wrong.

property owners¶

add_owner(tn, tid)[source]¶: Add tn as owner of this Tensor - it’s tag and ind maps will be updated whenever this tensor is retagged or reindexed.

remove_owner(tn)[source]¶: Remove TensorNetwork tn as an owner of this Tensor.

check_owners()[source]¶: Check if this tensor is ‘owned’ by any alive TensorNetworks. Also trim any weakrefs to dead TensorNetworks.

_apply_function(fn)[source]¶

modify(**kwargs)[source]¶

Overwrite the data of this tensor in place.

Parameters:

data (array, optional) – New data.
apply (callable, optional) – A function to apply to the current data. If data is also given this is applied subsequently.
inds (sequence of str, optional) – New tuple of indices.
tags (sequence of str, optional) – New tags.
left_inds (sequence of str, optional) – New grouping of indices to be ‘on the left’.

apply_to_arrays(fn)[source]¶: Apply the function fn to the underlying data array(s). This is meant for changing how the raw arrays are backed (e.g. converting between dtypes or libraries) but not their ‘numerical meaning’.

isel(selectors, inplace=False)[source]¶

Select specific values for some dimensions/indices of this tensor, thereby removing them. Analogous to X[:, :, 3, :, :] with arrays. The indices to select from can be specified either by integer, in which case the correspoding index is removed, or by a slice.

Parameters:

selectors (dict[str, int or slice or "r"]) –
Mapping of index(es) to which value to take. The values can be:
- int: select a specific value for that index.
- slice: select a range of values for that index.
- ”r”: contract a random vector in.
The mapping can contain indices that don’t appear on this tensor, in which case they are ignored.
inplace (bool, optional) – Whether to select inplace or not.

Return type:

Tensor.expand_ind, new_bond

Examples

>>> T = rand_tensor((2, 3, 4), inds=('a', 'b', 'c'))
>>> T.isel({'b': -1})
Tensor(shape=(2, 4), inds=('a', 'c'), tags=())

isel_[source]¶

add_tag(tag)[source]¶: Add a tag or multiple tags to this tensor. Unlike self.tags.add this also updates any TensorNetwork objects viewing this Tensor.

expand_ind(ind, size, mode=None, rand_strength=None, rand_dist='normal')[source]¶

Inplace increase the size of the dimension of ind, the new array entries will be filled with zeros by default.

Parameters:

name (str) – Name of the index to expand.
size (int, optional) – Size of the expanded index.
mode ({None, 'zeros', 'repeat', 'random'}, optional) – How to fill any new array entries. If 'zeros' then fill with zeros, if 'repeat' then repeatedly tile the existing entries. If 'random' then fill with random entries drawn from rand_dist, multiplied by rand_strength. If None then select from zeros or random depening on non-zero rand_strength.
rand_strength (float, optional) – If mode='random', a multiplicative scale for the random entries, defaulting to 1.0. If mode is None then supplying a non-zero value here triggers mode='random'.
rand_dist ({'normal', 'uniform', 'exp'}, optional) – If mode='random', the distribution to draw the random entries from.

new_ind(name, size=1, axis=0, mode=None, rand_strength=None, rand_dist='normal')[source]¶

Inplace add a new index - a named dimension. If size is specified to be greater than one then the new array entries will be filled with zeros.

Parameters:

name (str) – Name of the new index.
size (int, optional) – Size of the new index.
axis (int, optional) – Position of the new index.
mode ({None, 'zeros', 'repeat', 'random'}, optional) – How to fill any new array entries. If 'zeros' then fill with zeros, if 'repeat' then repeatedly tile the existing entries. If 'random' then fill with random entries drawn from rand_dist, multiplied by rand_strength. If None then select from zeros or random depening on non-zero rand_strength.
rand_strength (float, optional) – If mode='random', a multiplicative scale for the random entries, defaulting to 1.0. If mode is None then supplying a non-zero value here triggers mode='random'.
rand_dist ({'normal', 'uniform', 'exp'}, optional) – If mode='random', the distribution to draw the random entries from.

See also

new_bond[source]¶

new_ind_with_identity(name, left_inds, right_inds, axis=0)[source]¶

Inplace add a new index, where the newly stacked array entries form the identity from left_inds to right_inds. Selecting 0 or 1 for the new index name thus is like ‘turning off’ this tensor if viewed as an operator.

Parameters:

name (str) – Name of the new index.
left_inds (tuple[str]) – Names of the indices forming the left hand side of the operator.
right_inds (tuple[str]) – Names of the indices forming the right hand side of the operator. The dimensions of these must match those of left_inds.
axis (int, optional) – Position of the new index.

new_ind_pair_with_identity(new_left_ind, new_right_ind, d, inplace=False)[source]¶

Expand this tensor with two new indices of size d, by taking an (outer) tensor product with the identity operator. The two new indices are added as axes at the start of the tensor.

Parameters:

new_left_ind (str) – Name of the new left index.
new_right_ind (str) – Name of the new right index.
d (int) – Size of the new indices.
inplace (bool, optional) – Whether to perform the expansion inplace.

Return type:

new_ind_pair_with_identity_[source]¶

conj(inplace=False)[source]¶: Conjugate this tensors data (does nothing to indices).

conj_[source]¶

property H¶: Conjugate this tensors data (does nothing to indices).

property shape¶: The size of each dimension.

property ndim¶: The number of dimensions.

property size¶: The total number of array elements.

property dtype¶: The data type of the array elements.

property dtype_name¶: The name of the data type of the array elements.

property backend¶: The backend inferred from the data.

iscomplex()[source]¶

astype(dtype, inplace=False)[source]¶: Change the type of this tensor to dtype.

astype_[source]¶

max_dim()[source]¶: Return the maximum size of any dimension, or 1 if scalar.

ind_size(ind)[source]¶: Return the size of dimension corresponding to ind.

inds_size(inds)[source]¶: Return the total size of dimensions corresponding to inds.

shared_bond_size(other)[source]¶: Get the total size of the shared index(es) with other.

inner_inds()[source]¶: Get all indices that appear on two or more tensors.

transpose(*output_inds, inplace=False)[source]¶

Transpose this tensor - permuting the order of both the data and the indices. This operation is mainly for ensuring a certain data layout since for most operations the specific order of indices doesn’t matter.

Note to compute the tranditional ‘transpose’ of an operator within a contraction for example, you would just use reindexing not this.

Parameters:

output_inds (sequence of str) – The desired output sequence of indices.
inplace (bool, optional) – Perform the tranposition inplace.

Returns:

tt – The transposed tensor.

Return type:

transpose_like, reindex

See also

transpose_[source]¶

transpose_like(other, inplace=False)[source]¶

Transpose this tensor to match the indices of other, allowing for one index to be different. E.g. if self.inds = ('a', 'b', 'c', 'x') and other.inds = ('b', 'a', 'd', 'c') then ‘x’ will be aligned with ‘d’ and the output inds will be ('b', 'a', 'x', 'c')

Parameters:

other (Tensor) – The tensor to match.
inplace (bool, optional) – Perform the tranposition inplace.

Returns:

tt – The transposed tensor.

Return type:

See also

transpose

transpose_like_[source]¶

moveindex(ind, axis, inplace=False)[source]¶

Move the index ind to position axis. Like transpose, this permutes the order of both the data and the indices and is mainly for ensuring a certain data layout since for most operations the specific order of indices doesn’t matter.

Parameters:

ind (str) – The index to move.
axis (int) – The new position to move ind to. Can be negative.
inplace (bool, optional) – Whether to perform the move inplace or not.

Return type:

moveindex_[source]¶

item()[source]¶: Return the scalar value of this tensor, if it has a single element.

trace(left_inds, right_inds, preserve_tensor=False, inplace=False)[source]¶

Trace index or indices left_inds with right_inds, removing them.

Parameters:

left_inds (str or sequence of str) – The left indices to trace, order matching right_inds.
right_inds (str or sequence of str) – The right indices to trace, order matching left_inds.
preserve_tensor (bool, optional) – If True, a tensor will be returned even if no indices remain.
inplace (bool, optional) – Perform the trace inplace.

Returns:

Return type:

Tensor or scalar

sum_reduce(ind, inplace=False)[source]¶

Sum over index ind, removing it from this tensor.

Parameters:

ind (str) – The index to sum over.
inplace (bool, optional) – Whether to perform the reduction inplace.

Return type:

sum_reduce_[source]¶

vector_reduce(ind, v, inplace=False)[source]¶

Contract the vector v with the index ind of this tensor, removing it.

Parameters:

ind (str) – The index to contract.
v (array_like) – The vector to contract with.
inplace (bool, optional) – Whether to perform the reduction inplace.

Return type:

vector_reduce_[source]¶

rand_reduce(ind, dtype=None, inplace=False, **kwargs)[source]¶: Contract the index ind of this tensor with a random vector, removing it.

rand_reduce_[source]¶

collapse_repeated(inplace=False)[source]¶: Take the diagonals of any repeated indices, such that each index only appears once.

collapse_repeated_[source]¶

contract(*others, output_inds=None, **opts)[source]¶

direct_product(other, sum_inds=(), inplace=False)[source]¶

direct_product_[source]¶

split(*args, **kwargs)[source]¶

compute_reduced_factor(side, left_inds, right_inds, **split_opts)[source]¶

distance(other, **contract_opts)[source]¶

distance_normalized[source]¶

gate(G, ind, preserve_inds=True, transposed=False, inplace=False)[source]¶

Gate this tensor - contract a matrix into one of its indices without changing its indices. Unlike contract, G is a raw array and the tensor remains with the same set of indices. This is like applying:

\[x \leftarrow G x\]

or if transposed=True:

\[x \leftarrow x G\]

Parameters:

G (2D array_like) – The matrix to gate the tensor index with.
ind (str) – Which index to apply the gate to.
preserve_inds (bool, optional) – If True, the order of the indices is preserved, otherwise the gated index will be left at the first axis, avoiding a transpose.
transposed (bool, optional) – If True, the gate is effectively transpose and applied, or equivalently, contracted to its left rather than right.

Return type:

Examples

Create a random tensor of 4 qubits:

>>> t = qtn.rand_tensor(
...    shape=[2, 2, 2, 2],
...    inds=['k0', 'k1', 'k2', 'k3'],
... )

Create another tensor with an X gate applied to qubit 2:

>>> Gt = t.gate(qu.pauli('X'), 'k2')

The contraction of these two tensors is now the expectation of that operator:

>>> t.H @ Gt
-4.108910576149794

gate_[source]¶

singular_values(left_inds, method='svd')[source]¶

Return the singular values associated with splitting this tensor according to left_inds.

Parameters:

left_inds (sequence of str) – A subset of this tensors indices that defines ‘left’.
method ({'svd', 'eig'}) – Whether to use the SVD or eigenvalue decomposition to get the singular values.

Returns:

The singular values.

Return type:

1d-array

entropy(left_inds, method='svd')[source]¶

Return the entropy associated with splitting this tensor according to left_inds.

Parameters:

left_inds (sequence of str) – A subset of this tensors indices that defines ‘left’.
method ({'svd', 'eig'}) – Whether to use the SVD or eigenvalue decomposition to get the singular values.

Return type:

retag(retag_map, inplace=False)[source]¶

Rename the tags of this tensor, optionally, in-place.

Parameters:

retag_map (dict-like) – Mapping of pairs {old_tag: new_tag, ...}.
inplace (bool, optional) – If False (the default), a copy of this tensor with the changed tags will be returned.

retag_[source]¶

reindex(index_map, inplace=False)[source]¶

Rename the indices of this tensor, optionally in-place.

Parameters:

index_map (dict-like) – Mapping of pairs {old_ind: new_ind, ...}.
inplace (bool, optional) – If False (the default), a copy of this tensor with the changed inds will be returned.

reindex_[source]¶

fuse(fuse_map, inplace=False)[source]¶

Combine groups of indices into single indices.

Parameters:: fuse_map (dict_like or sequence of tuples.) – Mapping like: {new_ind: sequence of existing inds, ...} or an ordered mapping like [(new_ind_1, old_inds_1), ...] in which case the output tensor’s fused inds will be ordered. In both cases the new indices are created at the minimum axis of any of the indices that will be fused.
Returns:: The transposed, reshaped and re-labeled tensor.
Return type:: Tensor

fuse_[source]¶

unfuse(unfuse_map, shape_map, inplace=False)[source]¶

Reshape single indices into groups of multiple indices

Parameters:

unfuse_map (dict_like or sequence of tuples.) – Mapping like: {existing_ind: sequence of new inds, ...} or an ordered mapping like [(old_ind_1, new_inds_1), ...] in which case the output tensor’s new inds will be ordered. In both cases the new indices are created at the old index’s position of the tensor’s shape
shape_map (dict_like or sequence of tuples) – Mapping like: {old_ind: new_ind_sizes, ...} or an ordered mapping like [(old_ind_1, new_ind_sizes_1), ...].

Returns:

The transposed, reshaped and re-labeled tensor

Return type:

unfuse_[source]¶

to_dense(*inds_seq, to_qarray=False)[source]¶: Convert this Tensor into an dense array, with a single dimension for each of inds in inds_seqs. E.g. to convert several sites into a density matrix: T.to_dense(('k0', 'k1'), ('b0', 'b1')).

to_qarray[source]¶

squeeze(include=None, exclude=None, inplace=False)[source]¶

Drop any singlet dimensions from this tensor.

Parameters:

inplace (bool, optional) – Whether modify the original or return a new tensor.
include (sequence of str, optional) – Only squeeze dimensions with indices in this list.
exclude (sequence of str, optional) – Squeeze all dimensions except those with indices in this list.
inplace – Whether to perform the squeeze inplace or not.

Return type:

squeeze_[source]¶

largest_element()[source]¶: Return the largest element, in terms of absolute magnitude, of this tensor.

idxmin(f=None)[source]¶

Get the index configuration of the minimum element of this tensor, optionally applying f first.

Parameters:: f (callable or str, optional) – If a callable, apply this function to the tensor data before finding the minimum element. If a string, apply autoray.do(f, data).
Returns:: Mapping of index names to their values at the minimum element.
Return type:: dict[str, int]

idxmax(f=None)[source]¶

Get the index configuration of the maximum element of this tensor, optionally applying f first.

Parameters:: f (callable or str, optional) – If a callable, apply this function to the tensor data before finding the maximum element. If a string, apply autoray.do(f, data).
Returns:: Mapping of index names to their values at the maximum element.
Return type:: dict[str, int]

norm(squared=False, **contract_opts)[source]¶

Frobenius norm of this tensor:

\[\|t\|_F = \sqrt{\mathrm{Tr} \left(t^{\dagger} t\right)}\]

where the trace is taken over all indices. Equivalent to the square root of the sum of squared singular values across any partition.

overlap(other, **contract_opts)[source]¶

Overlap of this tensor with another tensor:

\[\langle o | t \rangle = \mathrm{Tr} \left(o^{\dagger} t\right)\]

where the trace is taken over all indices.

Parameters:: other (Tensor or TensorNetwork) – The other tensor or network to overlap with. This tensor will be conjugated.
Return type:: scalar

normalize(inplace=False)[source]¶

normalize_[source]¶

symmetrize(ind1, ind2, inplace=False)[source]¶: Hermitian symmetrize this tensor for indices ind1 and ind2. I.e. T = (T + T.conj().T) / 2, where the transpose is taken only over the specified indices.

symmetrize_[source]¶

isometrize(left_inds=None, method='qr', inplace=False)[source]¶

Make this tensor unitary (or isometric) with respect to left_inds. The underlying method is set by method.

Parameters:

left_inds (sequence of str) – The indices to group together and treat as the left hand side of a matrix.
method (str, optional) –
The method used to generate the isometry. The options are:
- ”qr”: use the Q factor of the QR decomposition of x with the constraint that the diagonal of R is positive.
- ”svd”: uses U @ VH of the SVD decomposition of x. This is useful for finding the ‘closest’ isometric matrix to x, such as when it has been expanded with noise etc. But is less stable for differentiation / optimization.
- ”exp”: use the matrix exponential of x - dag(x), first completing x with zeros if it is rectangular. This is a good parametrization for optimization, but more expensive for non-square x.
- ”cayley”: use the Cayley transform of x - dag(x), first completing x with zeros if it is rectangular. This is a good parametrization for optimization (one the few compatible with HIPS/autograd e.g.), but more expensive for non-square x.
- ”householder”: use the Householder reflection method directly. This requires that the backend implements “linalg.householder_product”.
- ”torch_householder”: use the Householder reflection method directly, using the torch_householder package. This requires that the package is installed and that the backend is "torch". This is generally the best parametrizing method for “torch” if available.
- ”mgs”: use a python implementation of the modified Gram Schmidt method directly. This is slow if not compiled but a useful reference.
Not all backends support all methods or differentiating through all methods.
inplace (bool, optional) – Whether to perform the unitization inplace.

Return type:

isometrize_[source]¶

unitize[source]¶

unitize_¶

randomize(dtype=None, inplace=False, **randn_opts)[source]¶

Randomize the entries of this tensor.

Parameters:

dtype ({None, str}, optional) – The data type of the random entries. If left as the default None, then the data type of the current array will be used.
inplace (bool, optional) – Whether to perform the randomization inplace, by default False.
randn_opts – Supplied to randn().

Return type:

randomize_[source]¶

flip(ind, inplace=False)[source]¶: Reverse the axis on this tensor corresponding to ind. Like performing e.g. X[:, :, ::-1, :].

flip_[source]¶

multiply_index_diagonal(ind, x, inplace=False)[source]¶: Multiply this tensor by 1D array x as if it were a diagonal tensor being contracted into index ind.

multiply_index_diagonal_[source]¶

almost_equals(other, **kwargs)[source]¶: Check if this tensor is almost the same as another.

drop_tags(tags=None)[source]¶: Drop certain tags, defaulting to all, from this tensor.

bonds(other)[source]¶: Return a tuple of the shared indices between this tensor and other.

bonds_size(other)[source]¶: Return the size of the shared indices between this tensor and other.

filter_bonds(other)[source]¶

Sort this tensor’s indices into a list of those that it shares and doesn’t share with another tensor.

Parameters:: other (Tensor) – The other tensor.
Returns:: shared, unshared – The shared and unshared indices.
Return type:: (tuple[str], tuple[str])

__imul__(other)[source]¶

__itruediv__(other)[source]¶

__and__(other)[source]¶: Combine with another Tensor or TensorNetwork into a new TensorNetwork.

__or__(other)[source]¶: Combine virtually (no copies made) with another Tensor or TensorNetwork into a new TensorNetwork.

__matmul__(other)[source]¶: Explicitly contract with another tensor. Avoids some slight overhead of calling the full tensor_contract().

negate(inplace=False)[source]¶: Negate this tensor.

negate_[source]¶

__neg__()[source]¶: Negate this tensor.

as_network(virtual=True)[source]¶: Return a TensorNetwork with only this tensor.

draw(*args, **kwargs)[source]¶: Plot a graph of this tensor and its indices.

graph[source]¶

visualize[source]¶

__getstate__()[source]¶

__setstate__(state)[source]¶

_repr_info()[source]¶: General info to show in various reprs. Sublasses can add more relevant info to this dict.

_repr_info_extra()[source]¶: General detailed info to show in various reprs. Sublasses can add more relevant info to this dict.

_repr_info_str(normal=True, extra=False)[source]¶: Render the general info as a string.

_repr_html_()[source]¶: Render this Tensor as HTML, for Jupyter notebooks.

__str__()[source]¶

__repr__()[source]¶

class quimb.tensor.TensorNetwork(ts=(), *, virtual=False, check_collisions=True)[source]¶

Bases: object

A collection of (as yet uncontracted) Tensors.

Parameters:

ts (sequence of Tensor or TensorNetwork) – The objects to combine. The new network will copy these (but not the underlying data) by default. For a view set virtual=True.
virtual (bool, optional) – Whether the TensorNetwork should be a view onto the tensors it is given, or a copy of them. E.g. if a virtual TN is constructed, any changes to a Tensor’s indices or tags will propagate to all TNs viewing that Tensor.
check_collisions (bool, optional) – If True, the default, then TensorNetwork instances with double indices which match another TensorNetwork instances double indices will have those indices’ names mangled. Can be explicitly turned off when it is known that no collisions will take place – i.e. when not adding any new tensors.

tensor_map¶

Mapping of unique ids to tensors, like``{tensor_id: tensor, …}``. I.e. this is where the tensors are ‘stored’ by the network.

Type:: dict

tag_map¶

Mapping of tags to a set of tensor ids which have those tags. I.e. {tag: {tensor_id_1, tensor_id_2, ...}}. Thus to select those tensors could do: map(tensor_map.__getitem__, tag_map[tag]).

Type:: dict

ind_map¶

Like tag_map but for indices. So ind_map[ind]] returns the tensor ids of those tensors with ind.

Type:: dict

exponent¶

A scalar prefactor for the tensor network, stored in base 10 like 10**exponent. This is mostly for conditioning purposes and will be 0.0 unless you use use equalize_norms(value) or tn.strip_exponent(tid_or_tensor).

Type:: float

_EXTRA_PROPS = ()¶

_CONTRACT_STRUCTURED = False¶

_tid_counter = 0¶

tensor_map¶

tag_map¶

ind_map¶

_inner_inds¶

_outer_inds¶

exponent = 0.0¶

combine(other, *, virtual=False, check_collisions=True)[source]¶

Combine this tensor network with another, returning a new tensor network. This can be overriden by subclasses to check for a compatible structured type.

Parameters:

other (TensorNetwork) – The other tensor network to combine with.
virtual (bool, optional) – Whether the new tensor network should copy all the incoming tensors (False, the default), or view them as virtual (True).
check_collisions (bool, optional) – Whether to check for index collisions between the two tensor networks before combining them. If True (the default), any inner indices that clash will be mangled.

Return type:

__and__(other)[source]¶: Combine this tensor network with more tensors, without contracting. Copies the tensors.

__or__(other)[source]¶: Combine this tensor network with more tensors, without contracting. Views the constituent tensors.

_update_properties(cls, like=None, current=None, **kwargs)[source]¶

classmethod new(like=None, **kwargs)[source]¶: Create a new tensor network, without any tensors, of type cls, with all the requisite properties specified by kwargs or inherited from like.

classmethod from_TN(tn, like=None, inplace=False, **kwargs)[source]¶

Construct a specific tensor network subclass (i.e. one with some promise about structure/geometry and tags/inds such as an MPS) from a generic tensor network which should have that structure already.

Parameters:

cls (class) – The TensorNetwork subclass to convert tn to.
tn (TensorNetwork) – The TensorNetwork to convert.
like (TensorNetwork, optional) – If specified, try and retrieve the neccesary attribute values from this tensor network.
inplace (bool, optional) – Whether to perform the conversion inplace or not.
kwargs – Extra properties of the TN subclass that should be specified.

view_as(cls, inplace=False, **kwargs)[source]¶: View this tensor network as subclass cls.

view_as_[source]¶

view_like(like, inplace=False, **kwargs)[source]¶: View this tensor network as the same subclass cls as like inheriting its extra properties as well.

view_like_[source]¶

copy(virtual=False, deep=False)[source]¶: Copy this TensorNetwork. If deep=False, (the default), then everything but the actual numeric data will be copied.

__copy__[source]¶

get_params()[source]¶: Get a pytree of the ‘parameters’, i.e. all underlying data arrays.

set_params(params)[source]¶: Take a pytree of the ‘parameters’, i.e. all underlying data arrays, as returned by get_params and set them.

_link_tags(tags, tid)[source]¶: Link tid to each of tags.

_unlink_tags(tags, tid)[source]¶: “Unlink tid from each of tags.

_link_inds(inds, tid)[source]¶: Link tid to each of inds.

_unlink_inds(inds, tid)[source]¶: “Unlink tid from each of inds.

_reset_inner_outer(inds)[source]¶

_next_tid()[source]¶

add_tensor(tensor, tid=None, virtual=False)[source]¶: Add a single tensor to this network - mangle its tid if neccessary.

add_tensor_network(tn, virtual=False, check_collisions=True)[source]¶

add(t, virtual=False, check_collisions=True)[source]¶: Add Tensor, TensorNetwork or sequence thereof to self.

make_tids_consecutive(tid0=0)[source]¶: Reset the tids - node identifies - to be consecutive integers.

__iand__(tensor)[source]¶: Inplace, but non-virtual, addition of a Tensor or TensorNetwork to this network. It should not have any conflicting indices.

__ior__(tensor)[source]¶: Inplace, virtual, addition of a Tensor or TensorNetwork to this network. It should not have any conflicting indices.

_modify_tensor_tags(old, new, tid)[source]¶

_modify_tensor_inds(old, new, tid)[source]¶

property num_tensors¶: The total number of tensors in the tensor network.

property num_indices¶: The total number of indices in the tensor network.

pop_tensor(tid)[source]¶: Remove tensor with tid from this network, and return it.

remove_all_tensors()[source]¶: Remove all tensors from this network.

_pop_tensor[source]¶

delete(tags, which='all')[source]¶

Delete any tensors which match all or any of tags.

Parameters:

tags (str or sequence of str) – The tags to match.
which ({'all', 'any'}, optional) – Whether to match all or any of the tags.

check()[source]¶: Check some basic diagnostics of the tensor network.

add_tag(tag, where=None, which='all')[source]¶: Add tag to every tensor in this network, or if where is specified, the tensors matching those tags – i.e. adds the tag to all tensors in self.select_tensors(where, which=which).

drop_tags(tags=None)[source]¶

Remove a tag or tags from this tensor network, defaulting to all. This is an inplace operation.

Parameters:: tags (str or sequence of str or None, optional) – The tag or tags to drop. If None, drop all tags.

retag(tag_map, inplace=False)[source]¶

Rename tags for all tensors in this network, optionally in-place.

Parameters:

tag_map (dict-like) – Mapping of pairs {old_tag: new_tag, ...}.
inplace (bool, optional) – Perform operation inplace or return copy (default).

retag_[source]¶

reindex(index_map, inplace=False)[source]¶

Rename indices for all tensors in this network, optionally in-place.

Parameters:: index_map (dict-like) – Mapping of pairs {old_ind: new_ind, ...}.

reindex_[source]¶

mangle_inner_(append=None, which=None)[source]¶

Generate new index names for internal bonds, meaning that when this tensor network is combined with another, there should be no collisions.

Parameters:

append (None or str, optional) – Whether and what to append to the indices to perform the mangling. If None a whole new random UUID will be generated.
which (sequence of str, optional) – Which indices to rename, if None (the default), all inner indices.

conj(mangle_inner=False, output_inds=None, inplace=False)[source]¶

Conjugate all the tensors in this network (leave all outer indices).

Parameters:

mangle_inner ({bool, str, None}, optional) – Whether to mangle the inner indices of the network. If a string is given, it will be appended to the index names.
output_inds (sequence of str, optional) – If given, the indices to mangle will be restricted to those not in this list. This is only needed for (hyper) tensor networks where output indices are not given simply by those that appear once.
inplace (bool, optional) – Whether to perform the conjugation inplace or not.

Return type:

conj_[source]¶

property H¶: Conjugate all the tensors in this network (leaves all indices).

item()[source]¶: Return the scalar value of this tensor network, if it is a scalar.

largest_element()[source]¶: Return the ‘largest element’, in terms of absolute magnitude, of this tensor network. This is defined as the product of the largest elements of each tensor in the network, which would be the largest single term occuring if the TN was summed explicitly.

make_norm(mangle_append='*', layer_tags=('KET', 'BRA'), output_inds=None, return_all=False)[source]¶

Make the norm (squared) tensor network of this tensor network tn.H & tn. This deterministally mangles the inner indices of the bra to avoid clashes with the ket, and also adds tags to the top and bottom layers. If the tensor network has hyper outer indices, you may need to specify the output indices. This allows ‘hyper’ norms.

Parameters:

mangle_append ({str, False or None}, optional) – How to mangle the inner indices of the bra.
layer_tags ((str, str), optional) – The tags to identify the top and bottom.
output_inds (sequence of str, optional) – If given, the indices to mangle will be restricted to those not in this list. This is only needed for (hyper) tensor networks where output indices are not given simply by those that appear once.
return_all (bool, optional) – Return the norm, the ket and the bra. These are virtual, i.e. are views of the same tensors.

Returns:

tn_norm

Return type:

norm(output_inds=None, squared=False, **contract_opts)[source]¶

Frobenius norm of this tensor network. Computed by exactly contracting the TN with its conjugate:

\[\|T\|_F = \sqrt{\mathrm{Tr} \left(T^{\dagger} T\right)}\]

where the trace is taken over all indices. Equivalent to the square root of the sum of squared singular values across any partition.

make_overlap(other, layer_tags=('KET', 'BRA'), output_inds=None, return_all=False)[source]¶

Make the overlap tensor network of this tensor network with another tensor network other.H & self. This deterministally mangles the inner indices of the bra to avoid clashes with the ket, and also adds tags to the top and bottom layers. If the tensor network has hyper outer indices, you may need to specify the output indices. This allows ‘hyper’ overlaps.

Parameters:

other (TensorNetwork) – The other tensor network to overlap with, it should have the same outer indices as this tensor network, all other indices will be explicitly mangled in the copy taken, allowing ‘hyper’ overlaps. This tensor network will be conjugated in the overlap.
layer_tags ((str, str), optional) – The tags to identify the top and bottom.
output_inds (sequence of str, optional) – If given, the indices to mangle will be restricted to those not in this list. This is only needed for (hyper) tensor networks where output indices are not given simply by those that appear once.
return_all (bool, optional) – Return the overlap, the ket and the bra. These are virtual, i.e. are views of the same tensors.

Returns:

tn_overlap

Return type:

get_symbol_map, get_equation

overlap(other, output_inds=None, **contract_opts)[source]¶

Overlap of this tensor network with another tensor network. Computed by exactly contracting the TN with the conjugate of the other TN:

\[\langle O, T \rangle = \mathrm{Tr} \left(O^{\dagger} T\right)\]

where the trace is taken over all indices. This supports ‘hyper’ tensor networks, where the output indices are not simply those that appear once.

Parameters:

other (TensorNetwork) – The other tensor network to overlap with, it should have the same outer indices as this tensor network, all other indices will be explicitly mangled in the copy taken, allowing ‘hyper’ overlaps. This tensor network will be conjugated in the overlap.
output_inds (sequence of str, optional) – If given, the indices to mangle will be restricted to those not in this list. This is only needed for (hyper) tensor networks where output indices are not given simply by those that appear once.
contract_opts – Supplied to tensor_contract() for the contraction.

Return type:

scalar

multiply(x, inplace=False, spread_over=8)[source]¶

Scalar multiplication of this tensor network with x.

Parameters:

x (scalar) – The number to multiply this tensor network by.
inplace (bool, optional) – Whether to perform the multiplication inplace.
spread_over (int, optional) – How many tensors to try and spread the multiplication over, in order that the effect of multiplying by a very large or small scalar is not concentrated.

multiply_[source]¶

multiply_each(x, inplace=False)[source]¶

Scalar multiplication of each tensor in this tensor network with x. If trying to spread a multiplicative factor fac uniformly over all tensors in the network and the number of tensors is large, then calling multiply(fac) can be inaccurate due to precision loss. If one has a routine that can precisely compute the x to be applied to each tensor, then this function avoids the potential inaccuracies in multiply().

Parameters:

x (scalar) – The number that multiplies each tensor in the network
inplace (bool, optional) – Whether to perform the multiplication inplace.

multiply_each_[source]¶

negate(inplace=False)[source]¶: Negate this tensor network.

negate_[source]¶

__mul__(other)[source]¶: Scalar multiplication.

__rmul__(other)[source]¶: Right side scalar multiplication.

__imul__(other)[source]¶: Inplace scalar multiplication.

__truediv__(other)[source]¶: Scalar division.

__itruediv__(other)[source]¶: Inplace scalar division.

__neg__()[source]¶: Negate this tensor network.

__iter__()[source]¶

property tensors¶: Get the tuple of tensors in this tensor network.

property arrays¶: Get the tuple of raw arrays containing all the tensor network data.

get_symbol_map()[source]¶: Get the mapping of the current indices to einsum style single unicode characters. The symbols are generated in the order they appear on the tensors.

See also

get_equation, get_inputs_output_size_dict

get_equation(output_inds=None)[source]¶

Get the ‘equation’ describing this tensor network, in einsum style with a single unicode letter per index. The symbols are generated in the order they appear on the tensors.

Parameters:: output_inds (None or sequence of str, optional) – Manually specify which are the output indices.
Returns:: eq
Return type:: str

Examples

>>> tn = qtn.TN_rand_reg(10, 3, 2)
>>> tn.get_equation()
'abc,dec,fgb,hia,jke,lfk,mnj,ing,omd,ohl->'

get_inputs_output_size_dict(output_inds=None)[source]¶

Get a tuple of inputs, output and size_dict suitable for e.g. passing to path optimizers. The symbols are generated in the order they appear on the tensors.

Parameters:

output_inds (None or sequence of str, optional) – Manually specify which are the output indices.

Returns:

inputs (tuple[str])
output (str)
size_dict (dict[str, ix])

See also

geometry_hash(output_inds=None, strict_index_order=False)[source]¶

A hash of this tensor network’s shapes & geometry. A useful check for determinism. Moreover, if this matches for two tensor networks then they can be contracted using the same tree for the same cost. Order of tensors matters for this - two isomorphic tensor networks with shuffled tensor order will not have the same hash value. Permuting the indices of individual of tensors or the output does not matter unless you set strict_index_order=True.

Parameters:

output_inds (None or sequence of str, optional) – Manually specify which indices are output indices and their order, otherwise assumed to be all indices that appear once.
strict_index_order (bool, optional) – If False, then the permutation of the indices of each tensor and the output does not matter.

Return type:

str

Examples

If we transpose some indices, then only the strict hash changes:

>>> tn = qtn.TN_rand_reg(100, 3, 2, seed=0)
>>> tn.geometry_hash()
'18c702b2d026dccb1a69d640b79d22f3e706b6ad'

>>> tn.geometry_hash(strict_index_order=True)
'c109fdb43c5c788c0aef7b8df7bb83853cf67ca1'

>>> t = tn['I0']
>>> t.transpose_(t.inds[2], t.inds[1], t.inds[0])
>>> tn.geometry_hash()
'18c702b2d026dccb1a69d640b79d22f3e706b6ad'

>>> tn.geometry_hash(strict_index_order=True)
'52c32c1d4f349373f02d512f536b1651dfe25893'

tensors_sorted()[source]¶: Return a tuple of tensors sorted by their respective tags, such that the tensors of two networks with the same tag structure can be iterated over pairwise.

apply_to_arrays(fn)[source]¶: Modify every tensor’s array inplace by applying fn to it. This is meant for changing how the raw arrays are backed (e.g. converting between dtypes or libraries) but not their ‘numerical meaning’.

_get_tids_from(xmap, xs, which)[source]¶

_get_tids_from_tags(tags, which='all')[source]¶

Return the set of tensor ids that match tags.

Parameters:

tags (seq or str, str, None, ..., int, slice) – Tag specifier(s).
which ({'all', 'any', '!all', '!any'}) –
How to select based on the tags, if:
- ’all’: get ids of tensors matching all tags
- ’any’: get ids of tensors matching any tags
- ’!all’: get ids of tensors not matching all tags
- ’!any’: get ids of tensors not matching any tags

Return type:

set[str]

_get_tids_from_inds(inds, which='all')[source]¶: Like _get_tids_from_tags but specify inds instead.

_tids_get(*tids)[source]¶: Convenience function that generates unique tensors from tids.

_inds_get(*inds)[source]¶: Convenience function that generates unique tensors from inds.

_tags_get(*tags)[source]¶: Convenience function that generates unique tensors from tags.

select_tensors(tags, which='all')[source]¶

Return the sequence of tensors that match tags. If which='all', each tensor must contain every tag. If which='any', each tensor can contain any of the tags.

Parameters:

tags (str or sequence of str) – The tag or tag sequence.
which ({'all', 'any'}) – Whether to require matching all or any of the tags.

Returns:

tagged_tensors – The tagged tensors.

Return type:

tuple of Tensor

_select_tids(tids, virtual=True)[source]¶: Get a copy or a virtual copy (doesn’t copy the tensors) of this TensorNetwork, only with the tensors corresponding to tids.

_select_without_tids(tids, virtual=True)[source]¶: Get a copy or a virtual copy (doesn’t copy the tensors) of this TensorNetwork, without the tensors corresponding to tids.

select(tags, which='all', virtual=True)[source]¶

Get a TensorNetwork comprising tensors that match all or any of tags, inherit the network properties/structure from self. This returns a view of the tensors not a copy.

Parameters:

tags (str or sequence of str) – The tag or tag sequence.
which ({'all', 'any'}) – Whether to require matching all or any of the tags.
virtual (bool, optional) – Whether the returned tensor network views the same tensors (the default) or takes copies (virtual=False) from self.

Returns:

tagged_tn – A tensor network containing the tagged tensors.

Return type:

select_tensors, partition_tensors

select_any[source]¶

select_all[source]¶

select_neighbors(tags, which='any')[source]¶

Select any neighbouring tensors to those specified by tags.self

Parameters:

tags (sequence of str, int) – Tags specifying tensors.
which ({'any', 'all'}, optional) – How to select tensors based on tags.

Returns:

The neighbouring tensors.

Return type:

tuple[Tensor]

See also

_select_local_tids(tids, max_distance=1, mode='graphdistance', fillin=False, grow_from='all', reduce_outer=None, virtual=True, include=None, exclude=None)[source]¶

Select a local region of tensors, based on graph distance or union of loops, from an initial set of tensor ids.

Parameters:

tids (sequence of str) – The initial tensor ids.
max_distance (int, optional) – The maximum distance to the initial tagged region, or if using ‘loopunion’ mode, the maximum size of any loop.
mode ({'graphdistance', 'loopunion'}, optional) – How to select the local tensors, either by graph distance or by selecting the union of all loopy regions containing tids.
fillin (bool or int, optional) – Whether to fill in the local patch with additional tensors, or not. fillin tensors are those connected by two or more bonds to the original local patch, the process is repeated int(fillin) times.
grow_from ({"all", "any"}, optional) – If mode is ‘loopunion’, whether each loop should contain all of the initial tids, or just any of them (generating a larger region).
reduce_outer ({'sum', 'svd', 'svd-sum', 'reflect'}, optional) – Whether and how to reduce any outer indices of the selected region.
virtual (bool, optional) – Whether the returned tensor network should be a view of the tensors or a copy.
include (None or sequence of int, optional) – If given, only include tensor from this set of tids.
exclude (None or sequence of int, optional) – If given, always exclude tensors from this set of tids.

Return type:

select_local(tags, which='all', max_distance=1, mode='graphdistance', fillin=False, grow_from='all', reduce_outer=None, virtual=True, include=None, exclude=None)[source]¶

Select a local region of tensors, based on graph distance max_distance to any tagged tensors.

Parameters:

tags (str or sequence of str) – The tag or tag sequence defining the initial region.
which ({'all', 'any', '!all', '!any'}, optional) – Whether to require matching all or any of the tags.
max_distance (int, optional) – The maximum distance to the initial tagged region, or if using ‘loopunion’ mode, the maximum size of any loop.
mode ({'graphdistance', 'loopunion'}, optional) – How to select the local tensors, either by graph distance or by selecting the union of all loopy regions containing where, of size up to max_distance, ensuring no dangling tensors.

fillin (bool or int, optional) –

Once the local region has been selected based on graph distance, whether and how many times to ‘fill-in’ corners by adding tensors connected multiple times. For example, if R is an initially tagged tensor and x are locally selected tensors:

  fillin=0       fillin=1       fillin=2

 | | | | |      | | | | |      | | | | |
-o-o-X-o-o-    -o-X-X-X-o-    -X-X-X-X-X-
 | | | | |      | | | | |      | | | | |
-o-X-X-X-o-    -X-X-X-X-X-    -X-X-X-X-X-
 | | | | |      | | | | |      | | | | |
-X-X-R-X-X-    -X-X-R-X-X-    -X-X-R-X-X-

grow_from ({"all", "any"}, optional) – If mode is ‘loopunion’, whether each loop should contain all of the initial tagged tensors, or just any of them (generating a larger region).
reduce_outer ({'sum', 'svd', 'svd-sum', 'reflect'}, optional) – Whether and how to reduce any outer indices of the selected region.
virtual (bool, optional) – Whether the returned tensor network should be a view of the tensors or a copy (virtual=False).
include (sequence of int, optional) – Only include tensor with these tids.
exclude (sequence of int, optional) – Only include tensor without these tids.

Return type:

select_path(loop, gauges=None)[source]¶

Select a sub tensor network corresponding to a single (possibly closed AKA loop like) path. Indices that are not part of the loop but do connect tids within it are cut, making this different to other select methods.

Parameters:

loop (NetworkPath or sequence of str or int) – A collection of tids and inds to select.
gauges (dict[str, array_like], optional) – A dictionary of gauge tensors to insert at dangling (including cut) indices.

Return type:

__getitem__(tags)[source]¶

Get the tensor(s) associated with tags.

Parameters:: tags (str or sequence of str) – The tags used to select the tensor(s).
Return type:: Tensor or sequence of Tensors

__setitem__(tags, tensor)[source]¶: Set the single tensor uniquely associated with tags.

__delitem__(tags)[source]¶: Delete any tensors which have all of tags.

partition_tensors(tags, inplace=False, which='any')[source]¶

Split this TN into a list of tensors containing any or all of tags and a TensorNetwork of the the rest.

Parameters:

tags (sequence of str) – The list of tags to filter the tensors by. Use ... (Ellipsis) to filter all.
inplace (bool, optional) – If true, remove tagged tensors from self, else create a new network with the tensors removed.
which ({'all', 'any'}) – Whether to require matching all or any of the tags.

Returns:

(u_tn, t_ts) – The untagged tensor network, and the sequence of tagged Tensors.

Return type:

(TensorNetwork, tuple of Tensors)

See also

partition, select, select_tensors

partition(tags, which='any', inplace=False)[source]¶

Split this TN into two, based on which tensors have any or all of tags. Unlike partition_tensors, both results are TNs which inherit the structure of the initial TN.

Parameters:

tags (sequence of str) – The tags to split the network with.
which ({'any', 'all'}) – Whether to split based on matching any or all of the tags.
inplace (bool) – If True, actually remove the tagged tensors from self.

Returns:

untagged_tn, tagged_tn – The untagged and tagged tensor networs.

Return type:

(TensorNetwork, TensorNetwork)

_split_tensor_tid(tid, left_inds, **split_opts)[source]¶

split_tensor(tags, left_inds, **split_opts)[source]¶: Split the single tensor uniquely identified by tags, adding the resulting tensors from the decomposition back into the network. Inplace operation.

replace_with_identity(where, which='any', inplace=False)[source]¶

Replace all tensors marked by where with an identity. E.g. if X denote where tensors:

---1  X--X--2---         ---1---2---
   |  |  |  |      ==>          |
   X--X--X  |                   |

Parameters:

where (tag or seq of tags) – Tags specifying the tensors to replace.
which ({'any', 'all'}) – Whether to replace tensors matching any or all the tags where.
inplace (bool) – Perform operation in place.

Returns:

The TN, with section replaced with identity.

Return type:

See also

replace_with_svd

replace_with_svd(where, left_inds, eps, *, which='any', right_inds=None, method='isvd', max_bond=None, absorb='both', cutoff_mode='rel', renorm=None, ltags=None, rtags=None, keep_tags=True, start=None, stop=None, inplace=False)[source]¶

Replace all tensors marked by where with an iteratively constructed SVD. E.g. if X denote where tensors:

                        :__       ___:
---X  X--X  X---        :  \     /   :
   |  |  |  |      ==>  :   U~s~VH---:
---X--X--X--X---        :__/     \   :
      |     +---        :         \__:
      X              left_inds       :
                                 right_inds

Parameters:

where (tag or seq of tags) – Tags specifying the tensors to replace.
left_inds (ind or sequence of inds) – The indices defining the left hand side of the SVD.
eps (float) – The tolerance to perform the SVD with, affects the number of singular values kept. See quimb.linalg.rand_linalg.estimate_rank().
which ({'any', 'all', '!any', '!all'}, optional) – Whether to replace tensors matching any or all the tags where, prefix with ‘!’ to invert the selection.
right_inds (ind or sequence of inds, optional) – The indices defining the right hand side of the SVD, these can be automatically worked out, but for hermitian decompositions the order is important and thus can be given here explicitly.
method (str, optional) – How to perform the decomposition, if not an iterative method the subnetwork dense tensor will be formed first, see tensor_split() for options.
max_bond (int, optional) – The maximum bond to keep, defaults to no maximum (-1).
ltags (sequence of str, optional) – Tags to add to the left tensor.
rtags (sequence of str, optional) – Tags to add to the right tensor.
keep_tags (bool, optional) – Whether to propagate tags found in the subnetwork to both new tensors or drop them, defaults to True.
start (int, optional) – If given, assume can use TNLinearOperator1D.
stop (int, optional) – If given, assume can use TNLinearOperator1D.
inplace (bool, optional) – Perform operation in place.

Return type:

See also

replace_with_identity

replace_with_svd_[source]¶

replace_section_with_svd(start, stop, eps, **replace_with_svd_opts)[source]¶

Take a 1D tensor network, and replace a section with a SVD. See replace_with_svd().

Parameters:

start (int) – Section start index.
stop (int) – Section stop index, not included itself.
eps (float) – Precision of SVD.
replace_with_svd_opts – Supplied to replace_with_svd().

Return type:

convert_to_zero()[source]¶: Inplace conversion of this network to an all zero tensor network.

_contract_between_tids(tid1, tid2, equalize_norms=False, gauges=None, output_inds=None, **contract_opts)[source]¶

contract_between(tags1, tags2, **contract_opts)[source]¶

Contract the two tensors specified by tags1 and tags2 respectively. This is an inplace operation. No-op if the tensor specified by tags1 and tags2 is the same tensor.

Parameters:

tags1 – Tags uniquely identifying the first tensor.
tags2 (str or sequence of str) – Tags uniquely identifying the second tensor.
contract_opts – Supplied to tensor_contract().

contract_ind(ind, output_inds=None, **contract_opts)[source]¶: Contract tensors connected by ind.

gate_inds[source]¶

gate_inds_[source]¶

gate_inds_with_tn(inds, gate, gate_inds_inner, gate_inds_outer, inplace=False)[source]¶

Gate some indices of this tensor network with another tensor network. That is, rewire and then combine them such that the new tensor network has the same outer indices as before, but now includes gate:

gate_inds_outer
 :
 :         gate_inds_inner
 :         :
 :         :   inds               inds
 :  ┌────┐ :   : ┌────┬───        : ┌───────┬───
 ───┤    ├──  a──┤    │          a──┤       │
    │    │       │    ├───          │       ├───
 ───┤gate├──  b──┤self│     -->  b──┤  new  │
    │    │       │    ├───          │       ├───
 ───┤    ├──  c──┤    │          c──┤       │
    └────┘       └────┴───          └───────┴───

Where there can be arbitrary structure of tensors within both self and gate.

The case where some of target inds are not present is handled as so (here ‘c’ is missing so ‘x’ and ‘y’ are kept):

gate_inds_outer
 :
 :         gate_inds_inner
 :         :
 :         :   inds               inds
 :  ┌────┐ :   : ┌────┬───        : ┌───────┬───
 ───┤    ├──  a──┤    │          a──┤       │
    │    │       │    ├───          │       ├───
 ───┤gate├──  b──┤self│     -->  b──┤  new  │
    │    │       │    ├───          │       ├───
x───┤    ├──y    └────┘          x──┤    ┌──┘
    └────┘                          └────┴───y

Which enables convinient construction of various tensor networks, for example propagators, from scratch.

Parameters:

inds (str or sequence of str) – The current indices to gate. If an index is not present on the target tensor network, it is ignored and instead the resulting tensor network will have both the corresponding inner and outer index of the gate tensor network.
gate (Tensor or TensorNetwork) – The tensor network to gate with.
gate_inds_inner (sequence of str) – The indices of gate to join to the old inds, must be the same length as inds.
gate_inds_outer (sequence of str) – The indices of gate to make the new outer inds, must be the same length as inds.

Returns:

tn_gated

Return type:

TensorNetwork.gate_inds

See also

gate_inds_with_tn_[source]¶

_compute_tree_gauges(tree, outputs)[source]¶

Given a tree of connected tensors, absorb the gauges from outside inwards, finally outputing the gauges associated with the outputs.

Parameters:

tree (sequence of (tid_outer, tid_inner, distance)) – The tree of connected tensors, see get_tree_span().
outputs (sequence of (tid, ind)) – Each output is specified by a tensor id and an index, such that having absorbed all gauges in the tree, the effective reduced factor of the tensor with respect to the index is returned.

Returns:

Gouts – The effective reduced factors of the tensor index pairs specified in outputs, each a matrix.

Return type:

sequence of array

_compress_between_virtual_tree_tids(tidl, tidr, max_bond, cutoff, r, absorb='both', include=None, exclude=None, span_opts=None, **compress_opts)[source]¶

_compute_bond_env(tid1, tid2, select_local_distance=None, select_local_opts=None, max_bond=None, cutoff=None, method='contract_around', contract_around_opts=None, contract_compressed_opts=None, optimize='auto-hq', include=None, exclude=None)[source]¶: Compute the local tensor environment of the bond(s), if cut, between two tensors.

_compress_between_full_bond_tids(tid1, tid2, max_bond, cutoff=0.0, absorb='both', renorm=False, method='eigh', select_local_distance=None, select_local_opts=None, env_max_bond='max_bond', env_cutoff='cutoff', env_method='contract_around', contract_around_opts=None, contract_compressed_opts=None, env_optimize='auto-hq', include=None, exclude=None)[source]¶

_compress_between_local_fit(tid1, tid2, max_bond, cutoff=0.0, absorb='both', method='als', select_local_distance=1, select_local_opts=None, include=None, exclude=None, **fit_opts)[source]¶

_compress_between_tids(tid1, tid2, max_bond=None, cutoff=1e-10, absorb='both', canonize_distance=None, canonize_opts=None, canonize_after_distance=None, canonize_after_opts=None, mode='basic', equalize_norms=False, gauges=None, gauge_smudge=1e-06, callback=None, **compress_opts)[source]¶

compress_between(tags1, tags2, max_bond=None, cutoff=1e-10, absorb='both', canonize_distance=0, canonize_opts=None, equalize_norms=False, **compress_opts)[source]¶

Compress the bond between the two single tensors in this network specified by tags1 and tags2 using tensor_compress_bond():

  |    |    |    |           |    |    |    |
==●====●====●====●==       ==●====●====●====●==
 /|   /|   /|   /|          /|   /|   /|   /|
  |    |    |    |           |    |    |    |
==●====1====2====●==  ==>  ==●====L----R====●==
 /|   /|   /|   /|          /|   /|   /|   /|
  |    |    |    |           |    |    |    |
==●====●====●====●==       ==●====●====●====●==
 /|   /|   /|   /|          /|   /|   /|   /|

This is an inplace operation. The compression is unlikely to be optimal with respect to the frobenius norm, unless the TN is already canonicalized at the two tensors. The absorb kwarg can be specified to yield an isometry on either the left or right resulting tensors.

Parameters:

tags1 – Tags uniquely identifying the first (‘left’) tensor.
tags2 (str or sequence of str) – Tags uniquely identifying the second (‘right’) tensor.
max_bond (int or None, optional) – The maxmimum bond dimension.
cutoff (float, optional) – The singular value cutoff to use.
canonize_distance (int, optional) – How far to locally canonize around the target tensors first.
canonize_opts (None or dict, optional) – Other options for the local canonization.
equalize_norms (bool or float, optional) – If set, rescale the norms of all tensors modified to this value, stripping the rescaling factor into the exponent attribute.
compress_opts – Supplied to tensor_compress_bond().

See also

canonize_between

compress_all(max_bond=None, cutoff=1e-10, canonize=True, tree_gauge_distance=None, canonize_distance=None, canonize_after_distance=None, mode='auto', inplace=False, **compress_opts)[source]¶

Compress all bonds one by one in this network.

Parameters:

max_bond (int or None, optional) – The maxmimum bond dimension to compress to.
cutoff (float, optional) – The singular value cutoff to use.
tree_gauge_distance (int, optional) – How far to include local tree gauge information when compressing. If the local geometry is a tree, then each compression will be locally optimal up to this distance.
canonize_distance (int, optional) – How far to locally canonize around the target tensors first, this is set automatically by tree_gauge_distance if not specified.
canonize_after_distance (int, optional) – How far to locally canonize around the target tensors after, this is set automatically by tree_gauge_distance, depending on mode if not specified.
mode ({'auto', 'basic', 'virtual-tree'}, optional) – The mode to use for compressing the bonds. If ‘auto’, will use ‘basic’ if tree_gauge_distance == 0 else ‘virtual-tree’.
inplace (bool, optional) – Whether to perform the compression inplace.
compress_opts – Supplied to compress_between().

Return type:

See also

compress_between, canonize_all

compress_all_[source]¶

compress_all_tree(inplace=False, **compress_opts)[source]¶: Canonically compress this tensor network, assuming it to be a tree. This generates a tree spanning out from the most central tensor, then compresses all bonds inwards in a depth-first manner, using an infinite canonize_distance to shift the orthogonality center.

compress_all_tree_[source]¶

compress_all_1d(max_bond=None, cutoff=1e-10, canonize=True, inplace=False, **compress_opts)[source]¶

Compress a tensor network that you know has a 1D topology, this proceeds by generating a spanning ‘tree’ from around the least central tensor, then optionally canonicalizing all bonds outwards and compressing inwards.

Parameters:

max_bond (int, optional) – The maximum bond dimension to compress to.
cutoff (float, optional) – The singular value cutoff to use.
canonize (bool, optional) – Whether to canonize all bonds outwards first.
inplace (bool, optional) – Whether to perform the compression inplace.
compress_opts – Supplied to tensor_compress_bond().

Return type:

compress_all_1d_[source]¶

compress_all_simple(max_bond=None, cutoff=1e-10, gauges=None, max_iterations=5, tol=0.0, smudge=1e-12, power=1.0, inplace=False, **gauge_simple_opts)[source]¶

compress_all_simple_[source]¶

_canonize_between_tids(tid1, tid2, absorb='right', gauges=None, gauge_smudge=1e-06, equalize_norms=False, **canonize_opts)[source]¶

canonize_between(tags1, tags2, absorb='right', **canonize_opts)[source]¶

‘Canonize’ the bond between the two single tensors in this network specified by tags1 and tags2 using tensor_canonize_bond:

  |    |    |    |           |    |    |    |
--●----●----●----●--       --●----●----●----●--
 /|   /|   /|   /|          /|   /|   /|   /|
  |    |    |    |           |    |    |    |
--●----1----2----●--  ==>  --●---->~~~~R----●--
 /|   /|   /|   /|          /|   /|   /|   /|
  |    |    |    |           |    |    |    |
--●----●----●----●--       --●----●----●----●--
 /|   /|   /|   /|          /|   /|   /|   /|

This is an inplace operation. This can only be used to put a TN into truly canonical form if the geometry is a tree, such as an MPS.

Parameters:

tags1 – Tags uniquely identifying the first (‘left’) tensor, which will become an isometry.
tags2 (str or sequence of str) – Tags uniquely identifying the second (‘right’) tensor.
absorb ({'left', 'both', 'right'}, optional) – Which side of the bond to absorb the non-isometric operator.
canonize_opts – Supplied to tensor_canonize_bond().

See also

compress_between

reduce_inds_onto_bond(inda, indb, tags=None, drop_tags=False, combine=True, ndim_cutoff=3)[source]¶: Use QR factorization to ‘pull’ the indices inda and indb off of their respective tensors and onto the bond between them. This is an inplace operation.

_get_neighbor_tids(tids, exclude_inds=())[source]¶

Get the tids of tensors connected to the tensor(s) at tids.

Parameters:

tids (int or sequence of int) – The tensor identifier(s) to get the neighbors of.
exclude_inds (sequence of str, optional) – Exclude these indices from being considered as connections.

Return type:

oset[int]

get_tid_neighbor_map()[source]¶: Get a mapping of each tensor id to the tensor ids of its neighbors.

_get_neighbor_inds(inds)[source]¶

Get the indices connected to the index(es) at inds.

Parameters:: inds (str or sequence of str) – The index(es) to get the neighbors of.
Return type:: oset[str]

_get_subgraph_tids(tids)[source]¶: Get the tids of tensors connected, by any distance, to the tensor or region of tensors tids.

_ind_to_subgraph_tids(ind)[source]¶: Get the tids of tensors connected, by any distance, to the index ind.

compute_centralities[source]¶

compute_hierarchical_grouping[source]¶

compute_hierarchical_linkage[source]¶

compute_hierarchical_ordering[source]¶

compute_hierarchical_ssa_path[source]¶

compute_shortest_distances[source]¶

gen_all_paths_between_tids[source]¶

gen_inds_connected[source]¶

gen_loops[source]¶

gen_patches[source]¶

gen_paths_loops[source]¶

gen_gloops[source]¶

get_local_patch[source]¶

get_path_between_tids[source]¶

get_loop_union[source]¶

get_tree_span[source]¶

isconnected[source]¶

istree[source]¶

least_central_tid[source]¶

most_central_tid[source]¶

subgraphs[source]¶

tids_are_connected[source]¶

_draw_tree_span_tids(tids, span=None, min_distance=0, max_distance=None, include=None, exclude=None, ndim_sort='max', distance_sort='min', sorter=None, weight_bonds=True, color='order', colormap='Spectral', **draw_opts)[source]¶

draw_tree_span(tags, which='all', min_distance=0, max_distance=None, include=None, exclude=None, ndim_sort='max', distance_sort='min', weight_bonds=True, color='order', colormap='Spectral', **draw_opts)[source]¶

Visualize a generated tree span out of the tensors tagged by tags.

Parameters:

tags (str or sequence of str) – Tags specifiying a region of tensors to span out of.
which ({'all', 'any': '!all', '!any'}, optional) – How to select tensors based on the tags.
min_distance (int, optional) – See get_tree_span().
max_distance (None or int, optional) – See get_tree_span().
include (sequence of str, optional) – See get_tree_span().
exclude (sequence of str, optional) – See get_tree_span().
distance_sort ({'min', 'max'}, optional) – See get_tree_span().
color ({'order', 'distance'}, optional) – Whether to color nodes based on the order of the contraction or the graph distance from the specified region.
colormap (str) – The name of a matplotlib colormap to use.

See also

get_tree_span

graph_tree_span[source]¶

_canonize_around_tids(tids, min_distance=0, max_distance=None, include=None, exclude=None, span_opts=None, absorb='right', gauge_links=False, link_absorb='both', inwards=True, gauges=None, gauge_smudge=1e-06, **canonize_opts)[source]¶

canonize_around(tags, which='all', min_distance=0, max_distance=None, include=None, exclude=None, span_opts=None, absorb='right', gauge_links=False, link_absorb='both', equalize_norms=False, inplace=False, **canonize_opts)[source]¶

Expand a locally canonical region around tags:

          --●---●--
        |   |   |   |
      --●---v---v---●--
    |   |   |   |   |   |
  --●--->---v---v---<---●--
|   |   |   |   |   |   |   |
●--->--->---O---O---<---<---●
|   |   |   |   |   |   |   |
  --●--->---^---^---^---●--
    |   |   |   |   |   |
      --●---^---^---●--
        |   |   |   |
          --●---●--

                 <=====>
                 max_distance = 2 e.g.

Shown on a grid here but applicable to arbitrary geometry. This is a way of gauging a tensor network that results in a canonical form if the geometry is described by a tree (e.g. an MPS or TTN). The canonizations proceed inwards via QR decompositions.

The sequence generated by round-robin expanding the boundary of the originally specified tensors - it will only be unique for trees.

Parameters:

tags (str, or sequence or str) – Tags defining which set of tensors to locally canonize around.
which ({'all', 'any', '!all', '!any'}, optional) – How select the tensors based on tags.
min_distance (int, optional) – How close, in terms of graph distance, to canonize tensors away. See get_tree_span().
max_distance (None or int, optional) – How far, in terms of graph distance, to canonize tensors away. See get_tree_span().
include (sequence of str, optional) – How to build the spanning tree to canonize along. See get_tree_span().
exclude (sequence of str, optional) – How to build the spanning tree to canonize along. See get_tree_span().
{'min' (distance_sort) – How to build the spanning tree to canonize along. See get_tree_span().
'max'} – How to build the spanning tree to canonize along. See get_tree_span().
optional – How to build the spanning tree to canonize along. See get_tree_span().
absorb ({'right', 'left', 'both'}, optional) – As we canonize inwards from tensor A to tensor B which to absorb the singular values into.
gauge_links (bool, optional) – Whether to gauge the links between branches of the spanning tree generated (in a Simple Update like fashion).
link_absorb ({'both', 'right', 'left'}, optional) – If performing the link gauging, how to absorb the singular values.
equalize_norms (bool or float, optional) – Scale the norms of tensors acted on to this value, accumulating the log10 scaled factors in self.exponent.
inplace (bool, optional) – Whether to perform the canonization inplace.

Return type:

See also

get_tree_span

canonize_around_[source]¶

gauge_all_canonize(max_iterations=5, absorb='both', gauges=None, gauge_smudge=1e-06, equalize_norms=False, inplace=False, **canonize_opts)[source]¶: Iterative gauge all the bonds in this tensor network with a basic ‘canonization’ strategy.

gauge_all_canonize_[source]¶

gauge_all_simple(max_iterations=5, tol=0.0, smudge=1e-12, power=1.0, damping=0.0, gauges=None, equalize_norms=False, touched_tids=None, info=None, progbar=False, inplace=False)[source]¶

Iterative gauge all the bonds in this tensor network with a ‘simple update’ like strategy. If gauges are not supplied they are initialized and then reabsorbed at the end, in which case this method acts as a kind of conditioning. More usefully, if you supply gauges then they will be updated inplace and not absorbed back into the tensor network, with the assumption that you are using/tracking them externally.

Parameters:

max_iterations (int, optional) – The maximum number of iterations to perform.
tol (float, optional) – The convergence tolerance for the singular values.
smudge (float, optional) – A small value to add to the singular values when gauging.
power (float, optional) – A power to raise the singular values to when gauging.
damping (float, optional) – The damping factor to apply to the gauging updates.
gauges (dict, optional) – Supply the initial gauges to use.
equalize_norms (bool, optional) – Whether to equalize the norms of the tensors after each update.
touched_tids (sequence of int, optional) – The tensor identifiers to start the gauge sweep from.
info (dict, optional) –
Store extra information about the gauging process in this dict. If supplied, the following keys are filled:
- ’iterations’: the number of iterations performed.
- ’max_sdiff’: the maximum singular value difference.
progbar (bool, optional) – Whether to show a progress bar.
inplace (bool, optional) – Whether to perform the gauging inplace.

Return type:

gauge_all_simple_[source]¶

gauge_all_random(max_iterations=1, unitary=True, seed=None, inplace=False)[source]¶: Gauge all the bonds in this network randomly. This is largely for testing purposes.

gauge_all_random_[source]¶

gauge_all(method='canonize', **gauge_opts)[source]¶

Gauge all bonds in this network using one of several strategies.

Parameters:

method (str, optional) – The method to use for gauging. One of “canonize”, “simple”, or “random”. Default is “canonize”.
gauge_opts (dict, optional) – Additional keyword arguments to pass to the chosen method.

gauge_all_[source]¶

_gauge_local_tids(tids, max_distance=1, mode='graphdistance', max_iterations='max_distance', method='canonize', include=None, exclude=None, **gauge_local_opts)[source]¶: Iteratively gauge all bonds in the local tensor network defined by tids according to one of several strategies.

gauge_local(tags, which='all', max_distance=1, max_iterations='max_distance', method='canonize', inplace=False, **gauge_local_opts)[source]¶: Iteratively gauge all bonds in the tagged sub tensor network according to one of several strategies.

gauge_local_[source]¶

gauge_simple_insert(gauges, remove=False, smudge=0.0, power=1.0)[source]¶

Insert the simple update style bond gauges found in gauges if they are present in this tensor network. The gauges inserted are also returned so that they can be removed later.

Parameters:

gauges (dict[str, array_like]) – The store of bond gauges, the keys being indices and the values being the vectors. Only bonds present in this dictionary will be gauged.
remove (bool, optional) – Whether to remove the gauges from the store after inserting them.
smudge (float, optional) – A small value to add to the gauge vectors to avoid singularities when inserting.
power (float, optional) – A power to raise the gauge vectors to when inserting.

Returns:

outer (list[(Tensor, str, array_like)]) – The sequence of gauges applied to outer indices, each a tuple of the tensor, the index and the gauge vector.
inner (list[((Tensor, Tensor), str, array_like)]) – The sequence of gauges applied to inner indices, each a tuple of the two inner tensors, the inner bond and the gauge vector applied.

static gauge_simple_remove(outer=None, inner=None)[source]¶: Remove the simple update style bond gauges inserted by gauge_simple_insert.

gauge_simple_temp(gauges, smudge=1e-12, power=1.0, ungauge_outer=True, ungauge_inner=True)[source]¶

Context manager that temporarily inserts simple update style bond gauges into this tensor network, before optionally ungauging them.

Parameters:

self (TensorNetwork) – The TensorNetwork to be gauge-bonded.
gauges (dict[str, array_like]) – The store of gauge bonds, the keys being indices and the values being the vectors. Only bonds present in this dictionary will be gauged.
smudge (float, optional) – A small value to add to the gauge vectors to avoid singularities.
power (float, optional) – A power to raise the gauge vectors to when inserting.
ungauge_outer (bool, optional) – Whether to ungauge the outer bonds.
ungauge_inner (bool, optional) – Whether to ungauge the inner bonds.

Yields:

outer (list[(Tensor, int, array_like)]) – The tensors, indices and gauges that were performed on outer indices.
inner (list[((Tensor, Tensor), int, array_like)]) – The tensors, indices and gauges that were performed on inner bonds.

Examples

>>> tn = TN_rand_reg(10, 4, 3)
>>> tn ^ all
-51371.66630218866

>>> gauges = {}
>>> tn.gauge_all_simple_(gauges=gauges)
>>> len(gauges)
20

>>> tn ^ all
28702551.673767876

>>> with gauged_bonds(tn, gauges):
...     # temporarily insert gauges
...     print(tn ^ all)
-51371.66630218887

>>> tn ^ all
28702551.67376789

_contract_compressed_tid_sequence(seq, *, output_inds=None, max_bond=None, cutoff=1e-10, tree_gauge_distance=1, canonize_distance=None, canonize_opts=None, canonize_after_distance=None, canonize_after_opts=None, gauge_boundary_only=True, compress_late=True, compress_mode='auto', compress_min_size=None, compress_span=False, compress_matrices=True, compress_exclude=None, compress_opts=None, strip_exponent=False, equalize_norms='auto', gauges=None, gauge_smudge=1e-06, callback_pre_contract=None, callback_post_contract=None, callback_pre_compress=None, callback_post_compress=None, callback=None, preserve_tensor=False, progbar=False, inplace=False)[source]¶: Core routine for performing compressed contraction.

_contract_around_tids(tids, seq=None, min_distance=0, max_distance=None, span_opts=None, max_bond=None, cutoff=1e-10, canonize_opts=None, **kwargs)[source]¶: Contract around tids, by following a greedily generated spanning tree, and compressing whenever two tensors in the outer ‘boundary’ share more than one index.

contract_around_center(**opts)[source]¶

contract_around_corner(**opts)[source]¶

contract_around(tags, which='all', min_distance=0, max_distance=None, span_opts=None, max_bond=None, cutoff=1e-10, tree_gauge_distance=1, canonize_distance=None, canonize_opts=None, canonize_after_distance=None, canonize_after_opts=None, gauge_boundary_only=True, compress_late=True, compress_min_size=None, compress_opts=None, compress_span=False, compress_matrices=True, equalize_norms=False, gauges=None, gauge_smudge=1e-06, callback_pre_contract=None, callback_post_contract=None, callback_pre_compress=None, callback_post_compress=None, callback=None, inplace=False, **kwargs)[source]¶: Perform a compressed contraction inwards towards the tensors identified by tags.

contract_around_[source]¶

contract_compressed(optimize, *, output_inds=None, max_bond='auto', cutoff=1e-10, tree_gauge_distance=1, canonize_distance=None, canonize_opts=None, canonize_after_distance=None, canonize_after_opts=None, gauge_boundary_only=True, compress_late=None, compress_mode='auto', compress_min_size=None, compress_span=True, compress_matrices=True, compress_exclude=None, compress_opts=None, strip_exponent=False, equalize_norms='auto', gauges=None, gauge_smudge=1e-06, callback_pre_contract=None, callback_post_contract=None, callback_pre_compress=None, callback_post_compress=None, callback=None, preserve_tensor=False, progbar=False, inplace=False, **kwargs)[source]¶

Contract this tensor network using the hyperoptimized approximate contraction method introduced in https://arxiv.org/abs/2206.07044.

Only supports non-hyper tensor networks.

Parameters:

optimize (str, sequence, HyperCompressedOptimizer, ContractionTreeCompressed) –
The contraction strategy to use. The options are:
- str: use the preset strategy with the given name,
- path_like: use this exact path,
- cotengra.HyperCompressedOptimizer: find the contraction using this optimizer
- cotengra.ContractionTreeCompressed: use this exact tree
Note that the strategy should be one that specifically targets compressed contraction, paths for exact contraction will likely perform badly. See the cotengra documentation for more details. Values for max_bond and compress_late are inherited from the optimizer if possible (and not specified).
output_inds (sequence of str, optional) – Output indices. Note that hyper indices are not supported and this is just for specifying the output order.
max_bond ("auto", int or None, optional) –
The maximum bond dimension to allow during compression.
- "auto": try and inherit value from the optimizer, or use the current maximum bond dimension squared if not available.
- int: a specific maximum bond dimension to use.
- None: no maximum bond dimension (compression still possible via cutoff) - not recommended.
cutoff (float, optional) – The singular value cutoff to use during compression.
tree_gauge_distance (int, optional) – The distance to ‘tree gauge’ around a pair of tensors before compressing. Depending on if compress_mode=”basic” this sets canonize_distance and canonize_after_distance.
canonize_distance (int, optional) – The distance to canonize around a pair of tensors before compressing.
canonize_opts (dict, optional) – Additional keyword arguments to pass to the canonize routine.
canonize_after_distance (int, optional) – The distance to canonize around a pair of tensors after compressing.
canonize_after_opts (dict, optional) – Additional keyword arguments to pass to the canonize routine after compressing.
gauge_boundary_only (bool, optional) – Whether to only gauge the ‘boundary’ tensors, that is, intermediate tensors.
compress_late (None or bool, optional) – Whether to compress just before contracting the tensors involved or immediately after. Early compression is cheaper and a better default especially for contractions beyond planar. Late compression leaves more information in the tensors for possibly better quality gauging and compression. Whilst the largest tensor (‘contraction width’) is typically unchanged, the total memory and cost can be quite a lot higher. By default, this is None, which will try and inherit the value from the optimizer, else default to False.
compress_mode ({'auto', 'basic', 'virtual-tree', ...}, optional) – How to compress a pair of tensors. If ‘auto’, then ‘basic’ is used if tree_gauge_distance=0 or gauges are supplied, otherwise ‘virtual-tree’ is used. See _compress_between_tids for other valid options.
compress_min_size (int, optional) – Skip compressing a pair of tensors if their contraction would yield a tensor smaller than this size.
compress_opts (dict, optional) – Additional keyword arguments to pass to the core pariwise compression routine.
compress_span (bool or int, optional) – Whether to compress between tensors that are going to be contracted. If an int, this specifies that if two tensors will be contracted in the next compress_span contractions, then their bonds should be compressed.
compress_matrices (bool, optional) – Whether to compress pairs of tensors that are effectively matrices.
compress_exclude (set[int], optional) – An explicit set of tensor ids to exclude from compression.
strip_exponent (bool, optional) – Whether the strip an overall exponent, log10, from the final contraction. If a TensorNetwork is returned, this exponent is accumulated in the exponent attribute. If a Tensor or scalar is returned, the exponent is returned separately.
equalize_norms (bool or "auto", optional) – Whether to equalize the norms of the tensors during the contraction. By default (“auto”) this follows strip_exponent. The overall scaling is accumulated, log10, into tn.exponent. If True, at the end this exponent is redistributed. If a float, this is the target norm to equalize tensors to, e.g. 1.0, and the exponent is not redistributed, which is useful in the case that the non-log value is beyond standard precision.
gauges (dict[str, array_like], optional) – If supplied, use simple update style gauges during the contraction. The keys should be indices and the values singular value vectors. Only bonds present in this dictionary will be gauged.
gauge_smudge (float, optional) – If using simple update style gauging, add a small value to the singular values to avoid singularities.
callback_pre_contract (callable, optional) – A function to call before contracting a pair of tensors. It should have signature fn(tn, (tid1, tid2)).
callback_post_contract (callable, optional) – A function to call after contracting a pair of tensors. It should have signature fn(tn, tid).
callback_pre_compress (callable, optional) – A function to call before compressing a pair of tensors. It should have signature fn(tn, (tid1, tid2)).
callback_post_compress (callable, optional) – A function to call after compressing a pair of tensors. It should have signature fn(tn, (tid1, tid2)).
callback (callable, optional) – A function to call after each full step of contraction and compressions. It should have signature fn(tn, tid).
preserve_tensor (bool, optional) – If True, return a Tensor object even if it represents a scalar. Ignore if inplace=True, in which case a TensorNetwork is always returned.
progbar (bool, optional) – Whether to show a progress bar.
inplace (bool, optional) – Whether to perform the contraction inplace.
kwargs (dict, optional) – Additional keyword passed to _contract_compressed_tid_sequence.

contract_compressed_[source]¶

new_bond(tags1, tags2, **opts)[source]¶

Inplace addition of a dummmy (size 1) bond between the single tensors specified by by tags1 and tags2.

Parameters:

tags1 (sequence of str) – Tags identifying the first tensor.
tags2 (sequence of str) – Tags identifying the second tensor.
opts – Supplied to new_bond().

See also

new_bond

_cut_between_tids(tid1, tid2, left_ind, right_ind)[source]¶

cut_between(left_tags, right_tags, left_ind, right_ind)[source]¶: Cut the bond between the tensors specified by left_tags and right_tags, giving them the new inds left_ind and right_ind respectively.

cut_bond(bond, new_left_ind=None, new_right_ind=None)[source]¶

Cut the bond index specified by bond between the tensors it connects. Use cut_between for control over which tensor gets which new index new_left_ind or new_right_ind. The index must connect exactly two tensors.

Parameters:

bond (str) – The index to cut.
new_left_ind (str, optional) – The new index to give to the left tensor (lowest tid value).
new_right_ind (str, optional) – The new index to give to the right tensor (highest tid value).

drape_bond_between(tagsa, tagsb, tags_target, left_ind=None, right_ind=None, inplace=False)[source]¶

Take the bond(s) connecting the tensors tagged at tagsa and tagsb, and ‘drape’ it through the tensor tagged at tags_target, effectively adding an identity tensor between the two and contracting it with the third:

 ┌─┐    ┌─┐      ┌─┐     ┌─┐
─┤A├─Id─┤B├─    ─┤A├─┐ ┌─┤B├─
 └─┘    └─┘      └─┘ │ │ └─┘
             left_ind│ │right_ind
     ┌─┐     -->     ├─┤
    ─┤C├─           ─┤D├─
     └┬┘             └┬┘     where D = C ⊗ Id
      │               │

This increases the size of the target tensor by d**2, and disconnects the tensors at tagsa and tagsb.

Parameters:

tagsa (str or sequence of str) – The tag(s) identifying the first tensor.
tagsb (str or sequence of str) – The tag(s) identifying the second tensor.
tags_target (str or sequence of str) – The tag(s) identifying the target tensor.
left_ind (str, optional) – The new index to give to the left tensor.
right_ind (str, optional) – The new index to give to the right tensor.
inplace (bool, optional) – Whether to perform the draping inplace.

Return type:

drape_bond_between_[source]¶

isel(selectors, inplace=False)[source]¶

Select specific values for some dimensions/indices of this tensor network, thereby removing them.

Parameters:

selectors (dict[str, int or slice or "r"]) –
Mapping of index(es) to which value to take. The values can be:
- int: select a specific value for that index.
- slice: select a range of values for that index.
- ”r”: contract a random vector in.
inplace (bool, optional) – Whether to select inplace or not.

Return type:

See also

Tensor.isel

isel_[source]¶

sum_reduce(ind, inplace=False)[source]¶

Sum over the index ind of this tensor network, removing it. This is like contracting a vector of ones in, or marginalizing a classical probability distribution.

Parameters:

ind (str) – The index to sum over.
inplace (bool, optional) – Whether to perform the reduction inplace.

sum_reduce_[source]¶

vector_reduce(ind, v, inplace=False)[source]¶

Contract the vector v with the index ind of this tensor network, removing it.

Parameters:

ind (str) – The index to contract.
v (array_like) – The vector to contract with.
inplace (bool, optional) – Whether to perform the reduction inplace.

Return type:

contract, contract_cumulative

vector_reduce_[source]¶

cut_iter(*inds)[source]¶

Cut and iterate over one or more indices in this tensor network. Each network yielded will have that index removed, and the sum of all networks will equal the original network. This works by iterating over the product of all combinations of each bond supplied to isel. As such, the number of networks produced is exponential in the number of bonds cut.

Parameters:: inds (sequence of str) – The bonds to cut.
Yields:: TensorNetwork

Examples

Here we’ll cut the two extra bonds of a cyclic MPS and sum the contraction of the resulting 49 OBC MPS norms:

>>> psi = MPS_rand_state(10, bond_dim=7, cyclic=True)
>>> norm = psi.H & psi
>>> bnds = bonds(norm[0], norm[-1])
>>> sum(tn ^ all for tn in norm.cut_iter(*bnds))
1.0

insert_operator(A, where1, where2, tags=None, inplace=False)[source]¶

Insert an operator on the bond between the specified tensors, e.g.:

  |   |              |   |
--1---2--    ->    --1-A-2--
  |                  |

Parameters:

A (array) – The operator to insert.
where1 (str, sequence of str, or int) – The tags defining the ‘left’ tensor.
where2 (str, sequence of str, or int) – The tags defining the ‘right’ tensor.
tags (str or sequence of str) – Tags to add to the new operator’s tensor.
inplace (bool, optional) – Whether to perform the insertion inplace.

insert_operator_[source]¶

_insert_gauge_tids(U, tid1, tid2, Uinv=None, tol=1e-10, bond=None)[source]¶

insert_gauge(U, where1, where2, Uinv=None, tol=1e-10)[source]¶

Insert the gauge transformation U^-1 @ U into the bond between the tensors, T1 and T2, defined by where1 and where2. The resulting tensors at those locations will be T1 @ U^-1 and U @ T2.

Parameters:

U (array) – The gauge to insert.
where1 (str, sequence of str, or int) – Tags defining the location of the ‘left’ tensor.
where2 (str, sequence of str, or int) – Tags defining the location of the ‘right’ tensor.
Uinv (array) – The inverse gauge, U @ Uinv == Uinv @ U == eye, to insert. If not given will be calculated using numpy.linalg.inv().

contract_tags(tags, which='any', output_inds=None, optimize=None, get=None, backend=None, strip_exponent=False, equalize_norms='auto', preserve_tensor=False, inplace=False, **contract_opts)[source]¶

Contract the tensors that match any or all of tags.

Parameters:

tags (sequence of str) – The list of tags to filter the tensors by. Use all or ... (Ellipsis) to contract all tensors.
which ({'all', 'any'}) – Whether to require matching all or any of the tags.
output_inds (sequence of str, optional) – The indices to specify as outputs of the contraction. If not given, and the tensor network has no hyper-indices, these are computed automatically as every index appearing once.
optimize (str, PathOptimizer, ContractionTree or path_like, optional) –
The contraction path optimization strategy to use.
- None: use the default strategy,
- str: use the preset strategy with the given name,
- cotengra.HyperOptimizer: find the contraction using this optimizer, supports slicing,
- opt_einsum.PathOptimizer: find the path using this optimizer.
- cotengra.ContractionTree: use this exact tree, supports slicing,
- path_like: use this exact path.
Contraction with cotengra might be a bit more efficient but the main reason would be to handle sliced contraction automatically.
get (str, optional) –
What to return. If:
- None (the default) - return the resulting scalar or Tensor.
- 'expression' - return a callbable expression that performs the contraction and operates on the raw arrays.
- 'tree' - return the cotengra.ContractionTree describing the contraction in detail.
- 'path' - return the raw ‘path’ as a list of tuples.
- 'symbol-map' - return the dict mapping indices to ‘symbols’ (single unicode letters) used internally by cotengra
- 'path-info' - return the opt_einsum.PathInfo path object with detailed information such as flop cost. The symbol-map is also added to the quimb_symbol_map attribute.
backend ({'auto', 'numpy', 'jax', 'cupy', 'tensorflow', ...}, optional) – Which backend to use to perform the contraction. Supplied to cotengra.
strip_exponent (bool, optional) – Whether the strip an overall exponent, log10, from the final contraction. If a TensorNetwork is returned, this exponent is accumulated in the exponent attribute. If a Tensor or scalar is returned, the exponent is returned separately.
equalize_norms (bool or "auto", optional) – Whether to equalize the norms of the tensors during the contraction. By default (“auto”) this follows strip_exponent.
preserve_tensor (bool, optional) – Whether to return a tensor regardless of whether the output object is a scalar (has no indices) or not.
inplace (bool, optional) – Whether to perform the contraction inplace.
contract_opts – Passed to tensor_contract().

Returns:

The result of the contraction, still a TensorNetwork if the contraction was only partial.

Return type:

TensorNetwork, Tensor or scalar

See also

contract_tags_[source]¶

contract(tags=..., output_inds=None, optimize=None, get=None, max_bond=None, strip_exponent=False, preserve_tensor=False, backend=None, inplace=False, **kwargs)[source]¶

Contract some, or all, of the tensors in this network. This method dispatches to contract_tags, contract_structured, or contract_compressed based on the various arguments.

Parameters:

tags (sequence of str, all, or Ellipsis, optional) – Any tensors with any of these tags with be contracted. Use all or ... (Ellipsis) to contract all tensors. ... will try and use a ‘structured’ contract method if possible.
output_inds (sequence of str, optional) – The indices to specify as outputs of the contraction. If not given, and the tensor network has no hyper-indices, these are computed automatically as every index appearing once.
optimize (str, PathOptimizer, ContractionTree or path_like, optional) –
The contraction path optimization strategy to use.
- None: use the default strategy,
- str: use the preset strategy with the given name,
- cotengra.HyperOptimizer: find the contraction using this optimizer, supports slicing,
- opt_einsum.PathOptimizer: find the path using this optimizer.
- cotengra.ContractionTree: use this exact tree, supports slicing,
- path_like: use this exact path.
Contraction with cotengra might be a bit more efficient but the main reason would be to handle sliced contraction automatically.
get (str, optional) –
What to return. If:
- None (the default) - return the resulting scalar or Tensor.
- 'expression' - return a callbable expression that performs the contraction and operates on the raw arrays.
- 'tree' - return the cotengra.ContractionTree describing the contraction in detail.
- 'path' - return the raw ‘path’ as a list of tuples.
- 'symbol-map' - return the dict mapping indices to ‘symbols’ (single unicode letters) used internally by cotengra
- 'path-info' - return the opt_einsum.PathInfo path object with detailed information such as flop cost. The symbol-map is also added to the quimb_symbol_map attribute.
strip_exponent (bool, optional) – Whether the strip an overall exponent, log10, from the final contraction. If a TensorNetwork is returned, this exponent is accumulated in the exponent attribute. If a Tensor or scalar is returned, the exponent is returned separately.
preserve_tensor (bool, optional) – Whether to return a tensor regardless of whether the output object is a scalar (has no indices) or not.
backend ({'auto', 'numpy', 'jax', 'cupy', 'tensorflow', ...}, optional) – Which backend to use to perform the contraction. Supplied to cotengra.
inplace (bool, optional) – Whether to perform the contraction inplace. This is only valid if not all tensors are contracted (which doesn’t produce a TN).
kwargs – Passed to tensor_contract(), contract_compressed() .

Returns:

The result of the contraction, still a TensorNetwork if the contraction was only partial.

Return type:

TensorNetwork, Tensor or scalar

See also

contract_tags, contract_cumulative

contract_[source]¶

contract_cumulative(tags_seq, output_inds=None, strip_exponent=False, equalize_norms='auto', preserve_tensor=False, inplace=False, **contract_opts)[source]¶

Cumulative contraction of tensor network. Contract the first set of tags, then that set with the next set, then both of those with the next and so forth. Could also be described as an manually ordered contraction of all tags in tags_seq.

Parameters:

tags_seq (sequence of sequence of str) – The list of tag-groups to cumulatively contract.
output_inds (sequence of str, optional) – The indices to specify as outputs of the contraction. If not given, and the tensor network has no hyper-indices, these are computed automatically as every index appearing once.
strip_exponent (bool, optional) – Whether the strip an overall exponent, log10, from the final contraction. If a TensorNetwork is returned, this exponent is accumulated in the exponent attribute. If a Tensor or scalar is returned, the exponent is returned separately.
equalize_norms (bool or "auto", optional) – Whether to equalize the norms of the tensors during the contraction. By default (“auto”) this follows strip_exponent.
preserve_tensor (bool, optional) – Whether to return a tensor regardless of whether the output object is a scalar (has no indices) or not.
inplace (bool, optional) – Whether to perform the contraction inplace.
contract_opts – Passed to tensor_contract().

Returns:

The result of the contraction, still a TensorNetwork if the contraction was only partial.

Return type:

TensorNetwork, Tensor or scalar

See also

contract, contract_tags

contraction_path(optimize=None, output_inds=None, **kwargs)[source]¶

Compute the contraction path, a sequence of (int, int), for the contraction of this entire tensor network using strategy optimize.

Parameters:

optimize (str, PathOptimizer, ContractionTree or path_like, optional) –
The contraction path optimization strategy to use.
- None: use the default strategy,
- str: use the preset strategy with the given name,
- cotengra.HyperOptimizer: find the contraction using this optimizer, supports slicing,
- opt_einsum.PathOptimizer: find the path using this optimizer.
- cotengra.ContractionTree: use this exact tree, supports slicing,
- path_like: use this exact path.
output_inds (sequence of str, optional) – The indices to specify as outputs of the contraction. If not given, and the tensor network has no hyper-indices, these are computed automatically as every index appearing once.
kwargs (dict, optional) – Passed to cotengra.array_contract_path().

Return type:

list[tuple[int, int]]

contraction_info(optimize=None, output_inds=None, **kwargs)[source]¶

Compute the opt_einsum.PathInfo object describing the contraction of this entire tensor network using strategy optimize. Note any sliced indices will be ignored.

Parameters:

optimize (str, PathOptimizer, ContractionTree or path_like, optional) –
The contraction path optimization strategy to use.
- None: use the default strategy,
- str: use the preset strategy with the given name,
- cotengra.HyperOptimizer: find the contraction using this optimizer, supports slicing,
- opt_einsum.PathOptimizer: find the path using this optimizer.
- cotengra.ContractionTree: use this exact tree, supports slicing,
- path_like: use this exact path.
output_inds (sequence of str, optional) – The indices to specify as outputs of the contraction. If not given, and the tensor network has no hyper-indices, these are computed automatically as every index appearing once.
kwargs (dict, optional) – Passed to cotengra.array_contract_tree().

Return type:

opt_einsum.PathInfo

contraction_tree(optimize=None, output_inds=None, **kwargs)[source]¶

Return the cotengra.ContractionTree corresponding to contracting this entire tensor network with strategy optimize.

Parameters:

optimize (str, PathOptimizer, ContractionTree or path_like, optional) –
The contraction path optimization strategy to use.
- None: use the default strategy,
- str: use the preset strategy with the given name,
- cotengra.HyperOptimizer: find the contraction using this optimizer, supports slicing,
- opt_einsum.PathOptimizer: find the path using this optimizer.
- cotengra.ContractionTree: use this exact tree, supports slicing,
- path_like: use this exact path.
output_inds (sequence of str, optional) – The indices to specify as outputs of the contraction. If not given, and the tensor network has no hyper-indices, these are computed automatically as every index appearing once.
kwargs (dict, optional) – Passed to cotengra.array_contract_tree().

Return type:

cotengra.ContractionTree

contraction_width(optimize=None, **contract_opts)[source]¶: Compute the ‘contraction width’ of this tensor network. This is defined as log2 of the maximum tensor size produced during the contraction sequence. If every index in the network has dimension 2 this corresponds to the maximum rank tensor produced.

contraction_cost(optimize=None, **contract_opts)[source]¶: Compute the ‘contraction cost’ of this tensor network. This is defined as log10 of the total number of scalar operations during the contraction sequence.

__rshift__(tags_seq)[source]¶: Overload of ‘>>’ for TensorNetwork.contract_cumulative.

__irshift__(tags_seq)[source]¶: Overload of ‘>>=’ for inplace TensorNetwork.contract_cumulative.

__xor__(tags)[source]¶: Overload of ‘^’ for TensorNetwork.contract.

__ixor__(tags)[source]¶: Overload of ‘^=’ for inplace TensorNetwork.contract.

__matmul__(other)[source]¶: Overload “@” to mean full contraction with another network.

as_network(virtual=True)[source]¶: Matching method (for ensuring object is a tensor network) to as_network(), which simply returns self if virtual=True.

aslinearoperator(left_inds, right_inds, ldims=None, rdims=None, backend=None, optimize=None)[source]¶: View this TensorNetwork as a TNLinearOperator.

split(left_inds, right_inds=None, **split_opts)[source]¶

Decompose this tensor network across a bipartition of outer indices.

This method matches Tensor.split by converting to a TNLinearOperator first. Note unless an iterative method is passed to method, the full dense tensor will be contracted.

trace(left_inds, right_inds, **contract_opts)[source]¶: Trace over left_inds joined with right_inds

to_dense(*inds_seq, to_qarray=False, **contract_opts)[source]¶: Convert this network into an dense array, with a single dimension for each of inds in inds_seqs. E.g. to convert several sites into a density matrix: TN.to_dense(('k0', 'k1'), ('b0', 'b1')).

to_qarray[source]¶

compute_reduced_factor(side, left_inds, right_inds, optimize='auto-hq', **contract_opts)[source]¶

Compute either the left or right ‘reduced factor’ of this tensor network. I.e., view as an operator, X, mapping left_inds to right_inds and compute L or R such that X = U_R @ R or X = L @ U_L, with U_R and U_L unitary operators that are not computed. Only dag(X) @ X or X @ dag(X) is contracted, which is generally cheaper than contracting X itself.

Parameters:

self (TensorNetwork) – The tensor network to compute the reduced factor of.
side ({'left', 'right'}) – Whether to compute the left or right reduced factor. If ‘right’ then dag(X) @ X is contracted, otherwise X @ dag(X).
left_inds (sequence of str) – The indices forming the left side of the operator.
right_inds (sequence of str) – The indices forming the right side of the operator.
contract_opts (dict, optional) – Options to pass to to_dense().

Return type:

array_like

insert_compressor_between_regions(ltags, rtags, max_bond=None, cutoff=1e-10, select_which='any', insert_into=None, new_tags=None, new_ltags=None, new_rtags=None, bond_ind=None, optimize='auto-hq', inplace=False, **compress_opts)[source]¶

Compute and insert a pair of ‘oblique’ projection tensors (see for example https://arxiv.org/abs/1905.02351) that effectively compresses between two regions of the tensor network. Useful for various approximate contraction methods such as HOTRG and CTMRG.

Parameters:

ltags (sequence of str) – The tags of the tensors in the left region.
rtags (sequence of str) – The tags of the tensors in the right region.
max_bond (int or None, optional) – The maximum bond dimension to use for the compression (i.e. shared by the two projection tensors). If None then the maximum is controlled by cutoff.
cutoff (float, optional) – The cutoff to use for the compression.
select_which ({'any', 'all', 'none'}, optional) – How to select the regions based on the tags, see select().
insert_into (TensorNetwork, optional) – If given, insert the new tensors into this tensor network, assumed to have the same relevant indices as self.
new_tags (str or sequence of str, optional) – The tag(s) to add to both the new tensors.
new_ltags (str or sequence of str, optional) – The tag(s) to add to the new left projection tensor.
new_rtags (str or sequence of str, optional) – The tag(s) to add to the new right projection tensor.
optimize (str or PathOptimizer, optional) – How to optimize the contraction of the projection tensors.
inplace (bool, optional) – Whether perform the insertion in-place. If insert_into is supplied then this doesn’t matter, and that tensor network will be modified and returned.

Return type:

compute_reduced_factor, select

See also

insert_compressor_between_regions_[source]¶

distance(*args, **kwargs)[source]¶

distance_normalized[source]¶

fit(tn_target, method='als', tol=1e-09, inplace=False, progbar=False, **fitting_opts)[source]¶

Optimize the entries of this tensor network with respect to a least squares fit of tn_target which should have the same outer indices. Depending on method this calls tensor_network_fit_als() or tensor_network_fit_autodiff(). The quantity minimized is:

\[D(A, B) = | A - B |_{\mathrm{fro}} = \mathrm{Tr} [(A - B)^{\dagger}(A - B)]^{1/2} = ( \langle A | A \rangle - 2 \mathrm{Re} \langle A | B \rangle| + \langle B | B \rangle ) ^{1/2}\]

Parameters:

tn_target (TensorNetwork) – The target tensor network to try and fit the current one to.
method ({'als', 'autodiff', 'tree'}, optional) –
How to perform the fitting. The options are:
- ’als’: alternating least squares (ALS) optimization,
- ’autodiff’: automatic differentiation optimization,
- ’tree’: ALS where the fitted tensor network has a tree structure and thus a canonical form can be utilized for much greater efficiency and stability.
Generally ALS is better for simple geometries, autodiff better for complex ones. Tree best if the tensor network has a tree structure.
tol (float, optional) – The target norm distance.
inplace (bool, optional) – Update the current tensor network in place.
progbar (bool, optional) – Show a live progress bar of the fitting process.
fitting_opts – Supplied to either tensor_network_fit_als(), tensor_network_fit_autodiff(), or tensor_network_fit_tree().

Returns:

tn_opt – The optimized tensor network.

Return type:

fit_[source]¶

property tags¶

all_inds()[source]¶: Return a tuple of all indices in this network.

ind_size(ind)[source]¶: Find the size of ind.

inds_size(inds)[source]¶: Return the total size of dimensions corresponding to inds.

ind_sizes()[source]¶: Get dict of each index mapped to its size.

inner_inds()[source]¶: Tuple of interior indices, assumed to be any indices that appear twice or more (this only holds generally for non-hyper tensor networks).

outer_inds()[source]¶: Tuple of exterior indices, assumed to be any lone indices (this only holds generally for non-hyper tensor networks).

outer_dims_inds()[source]¶: Get the ‘outer’ pairs of dimension and indices, i.e. as if this tensor network was fully contracted.

outer_size()[source]¶: Get the total size of the ‘outer’ indices, i.e. as if this tensor network was fully contracted.

get_multibonds(include=None, exclude=None)[source]¶

Get a dict of ‘multibonds’ in this tensor network, i.e. groups of two or more indices that appear on exactly the same tensors and thus could be fused, for example.

Parameters:

include (sequence of str, optional) – Only consider these indices, by default all indices.
exclude (sequence of str, optional) – Ignore these indices, by default the outer indices of this TN.

Returns:

A dict mapping the tuple of indices that could be fused to the tuple of tensor ids they appear on.

Return type:

dict[tuple[str], tuple[int]]

get_hyperinds(output_inds=None)[source]¶

Get a tuple of all ‘hyperinds’, defined as those indices which don’t appear exactly twice on either the tensors or in the ‘outer’ (i.e. output) indices.

Note the default set of ‘outer’ indices is calculated as only those indices that appear once on the tensors, so these likely need to be manually specified, otherwise, for example, an index that appears on two tensors and the output will incorrectly be identified as non-hyper.

Parameters:: output_inds (None, str or sequence of str, optional) – The outer or output index or indices. If not specified then taken as every index that appears only once on the tensors (and thus non-hyper).
Returns:: The tensor network hyperinds.
Return type:: tuple[str]

compute_contracted_inds(*tids, output_inds=None)[source]¶: Get the indices describing the tensor contraction of tensors corresponding to tids.

squeeze(fuse=False, include=None, exclude=None, inplace=False)[source]¶

Drop singlet bonds and dimensions from this tensor network. If fuse=True also fuse all multibonds between tensors.

Parameters:

fuse (bool, optional) – Whether to fuse multibonds between tensors as well as squeezing.
include (sequence of str, optional) – Only squeeze these indices, by default all indices.
exclude (sequence of str, optional) – Ignore these indices, by default the outer indices of this TN.
inplace (bool, optional) – Whether to perform the squeeze and optional fuse inplace.

Return type:

squeeze_[source]¶

isometrize(method='qr', allow_no_left_inds=False, inplace=False)[source]¶

Project every tensor in this network into an isometric form, assuming they have left_inds marked.

Parameters:

method (str, optional) –
The method used to generate the isometry. The options are:
- ”qr”: use the Q factor of the QR decomposition of x with the constraint that the diagonal of R is positive.
- ”svd”: uses U @ VH of the SVD decomposition of x. This is useful for finding the ‘closest’ isometric matrix to x, such as when it has been expanded with noise etc. But is less stable for differentiation / optimization.
- ”exp”: use the matrix exponential of x - dag(x), first completing x with zeros if it is rectangular. This is a good parametrization for optimization, but more expensive for non-square x.
- ”cayley”: use the Cayley transform of x - dag(x), first completing x with zeros if it is rectangular. This is a good parametrization for optimization (one the few compatible with HIPS/autograd e.g.), but more expensive for non-square x.
- ”householder”: use the Householder reflection method directly. This requires that the backend implements “linalg.householder_product”.
- ”torch_householder”: use the Householder reflection method directly, using the torch_householder package. This requires that the package is installed and that the backend is "torch". This is generally the best parametrizing method for “torch” if available.
- ”mgs”: use a python implementation of the modified Gram Schmidt method directly. This is slow if not compiled but a useful reference.
Not all backends support all methods or differentiating through all methods.
allow_no_left_inds (bool, optional) – If True then allow tensors with no left_inds to be left alone, rather than raising an error.
inplace (bool, optional) – If True then perform the operation in-place.

Return type:

isometrize_[source]¶

unitize[source]¶

unitize_¶

randomize(dtype=None, seed=None, inplace=False, **randn_opts)[source]¶

Randomize every tensor in this TN - see quimb.tensor.tensor_core.Tensor.randomize().

Parameters:

dtype ({None, str}, optional) – The data type of the random entries. If left as the default None, then the data type of the current array will be used.
seed (None or int, optional) – Seed for the random number generator.
inplace (bool, optional) – Whether to perform the randomization inplace, by default False.
randn_opts – Supplied to randn().

Return type:

randomize_[source]¶

strip_exponent(tid_or_tensor, value=None, check_zero=False)[source]¶

Scale the elements of tensor corresponding to tid so that the norm of the array is some value, which defaults to 1. The log of the scaling factor, base 10, is then accumulated in the exponent attribute.

Parameters:

tid (str or Tensor) – The tensor identifier or actual tensor.
value (None or float, optional) – The value to scale the norm of the tensor to.
check_zero (bool, optional) – Whether to check if the tensor has zero norm and in that case do nothing, since the exponent would be -inf. Off by default to avoid data dependent computational graphs when tracing and computing gradients etc.

distribute_exponent()[source]¶: Distribute the exponent p of this tensor network (i.e. corresponding to tn * 10**p) equally among all tensors.

equalize_norms(value=None, check_zero=False, inplace=False)[source]¶

Make the Frobenius norm of every tensor in this TN equal without changing the overall value if value=None, or set the norm of every tensor to value by scalar multiplication only.

Parameters:

value (None or float, optional) – Set the norm of each tensor to this value specifically. If supplied the change in overall scaling will be accumulated in tn.exponent in the form of a base 10 power.
check_zero (bool, optional) – Whether, if and when equalizing norms, to check if tensors have zero norm and in that case do nothing, since the exponent would be -inf. Off by default to avoid data dependent computational graphs when tracing and computing gradients etc.
inplace (bool, optional) – Whether to perform the norm equalization inplace or not.

Return type:

equalize_norms_[source]¶

balance_bonds(inplace=False)[source]¶

Apply tensor_balance_bond() to all bonds in this tensor network.

Parameters:: inplace (bool, optional) – Whether to perform the bond balancing inplace or not.
Return type:: TensorNetwork

balance_bonds_[source]¶

fuse_multibonds(gauges=None, include=None, exclude=None, inplace=False)[source]¶

Fuse any multi-bonds (more than one index shared by the same pair of tensors) into a single bond.

Parameters:

gauges (None or dict[str, array_like], optional) – If supplied, also fuse the gauges contained in this dict.
include (sequence of str, optional) – Only consider these indices, by default all indices.
exclude (sequence of str, optional) – Ignore these indices, by default the outer indices of this TN.

fuse_multibonds_[source]¶

expand_bond_dimension(new_bond_dim, mode=None, rand_strength=None, rand_dist='normal', inds_to_expand=None, inplace=False)[source]¶

Increase the dimension of all or some of the bonds in this tensor network to at least new_bond_dim, optinally adding some random noise to the new entries.

Parameters:

new_bond_dim (int) – The minimum bond dimension to expand to, if the bond dimension is already larger than this it will be left unchanged.
rand_strength (float, optional) – The strength of random noise to add to the new array entries, if any. The noise is drawn from a normal distribution with standard deviation rand_strength.
inds_to_expand (sequence of str, optional) – The indices to expand, if not all.
inplace (bool, optional) – Whether to expand this tensor network in place, or return a new one.

Return type:

expand_bond_dimension_[source]¶

flip(inds, inplace=False)[source]¶: Flip the dimension corresponding to indices inds on all tensors that share it.

flip_[source]¶

rank_simplify(output_inds=None, equalize_norms=False, cache=None, max_combinations=500, check_zero=False, inplace=False)[source]¶

Simplify this tensor network by performing contractions that don’t increase the rank of any tensors.

Parameters:

output_inds (sequence of str, optional) – Explicitly set which indices of the tensor network are output indices and thus should not be modified.
equalize_norms (bool or float) – Actively renormalize the tensors during the simplification process. Useful for very large TNs. The scaling factor will be stored as an exponent in tn.exponent.
cache (None or set) – Persistent cache used to mark already checked tensors.
check_zero (bool, optional) – Whether, if and when equalizing norms, to check if tensors have zero norm and in that case do nothing, since the exponent would be -inf. Off by default to avoid data dependent computational graphs when tracing and computing gradients etc.
inplace (bool, optional) – Whether to perform the rand reduction inplace.

Return type:

rank_simplify_[source]¶

diagonal_reduce(output_inds=None, atol=1e-12, cache=None, inplace=False)[source]¶

Find tensors with diagonal structure and collapse those axes. This will create a tensor ‘hyper’ network with indices repeated 2+ times, as such, output indices should be explicitly supplied when contracting, as they can no longer be automatically inferred. For example:

>>> tn_diag = tn.diagonal_reduce()
>>> tn_diag.contract(all, output_inds=[])

Parameters:

output_inds (sequence of str, optional) – Which indices to explicitly consider as outer legs of the tensor network and thus not replace. If not given, these will be taken as all the indices that appear once.
atol (float, optional) – When identifying diagonal tensors, the absolute tolerance with which to compare to zero with.
cache (None or set) – Persistent cache used to mark already checked tensors.
inplace – Whether to perform the diagonal reduction inplace.
bool – Whether to perform the diagonal reduction inplace.
optional – Whether to perform the diagonal reduction inplace.

Return type:

diagonal_reduce_[source]¶

antidiag_gauge(output_inds=None, atol=1e-12, cache=None, inplace=False)[source]¶

Flip the order of any bonds connected to antidiagonal tensors. Whilst this is just a gauge fixing (with the gauge being the flipped identity) it then allows diagonal_reduce to then simplify those indices.

Parameters:

output_inds (sequence of str, optional) – Which indices to explicitly consider as outer legs of the tensor network and thus not flip. If not given, these will be taken as all the indices that appear once.
atol (float, optional) – When identifying antidiagonal tensors, the absolute tolerance with which to compare to zero with.
cache (None or set) – Persistent cache used to mark already checked tensors.
inplace – Whether to perform the antidiagonal gauging inplace.
bool – Whether to perform the antidiagonal gauging inplace.
optional – Whether to perform the antidiagonal gauging inplace.

Return type:

antidiag_gauge_[source]¶

column_reduce(output_inds=None, atol=1e-12, cache=None, inplace=False)[source]¶

Find bonds on this tensor network which have tensors where all but one column (of the respective index) is non-zero, allowing the ‘cutting’ of that bond.

Parameters:

output_inds (sequence of str, optional) – Which indices to explicitly consider as outer legs of the tensor network and thus not slice. If not given, these will be taken as all the indices that appear once.
atol (float, optional) – When identifying singlet column tensors, the absolute tolerance with which to compare to zero with.
cache (None or set) – Persistent cache used to mark already checked tensors.
inplace – Whether to perform the column reductions inplace.
bool – Whether to perform the column reductions inplace.
optional – Whether to perform the column reductions inplace.

Return type:

column_reduce_[source]¶

split_simplify(atol=1e-12, equalize_norms=False, cache=None, check_zero=False, inplace=False, **split_opts)[source]¶

Find tensors which have low rank SVD decompositions across any combination of bonds and perform them.

Parameters:

atol (float, optional) – Cutoff used when attempting low rank decompositions.
equalize_norms (bool or float) – Actively renormalize the tensors during the simplification process. Useful for very large TNs. The scaling factor will be stored as an exponent in tn.exponent.
cache (None or set) – Persistent cache used to mark already checked tensors.
check_zero (bool, optional) – Whether, if and when equalizing norms, to check if tensors have zero norm and in that case do nothing, since the exponent would be -inf. Off by default to avoid data dependent computational graphs when tracing and computing gradients etc.
inplace – Whether to perform the split simplification inplace.
bool – Whether to perform the split simplification inplace.
optional – Whether to perform the split simplification inplace.

split_simplify_[source]¶

pair_simplify(cutoff=1e-12, output_inds=None, max_inds=10, cache=None, equalize_norms=False, max_combinations=500, check_zero=False, inplace=False, **split_opts)[source]¶

pair_simplify_[source]¶

loop_simplify(output_inds=None, max_loop_length=None, max_inds=10, cutoff=1e-12, loops=None, cache=None, equalize_norms=False, check_zero=False, inplace=False, **split_opts)[source]¶

Try and simplify this tensor network by identifying loops and checking for low-rank decompositions across groupings of the loops outer indices.

Parameters:

max_loop_length (None or int, optional) – Largest length of loop to search for, if not set, the size will be set to the length of the first (and shortest) loop found.
cutoff (float, optional) – Cutoff to use for the operator decomposition.
loops (None, sequence or callable) – Loops to check, or a function that generates them.
cache (set, optional) – For performance reasons can supply a cache for already checked loops.
check_zero (bool, optional) – Whether, if and when equalizing norms, to check if tensors have zero norm and in that case do nothing, since the exponent would be -inf. Off by default to avoid data dependent computational graphs when tracing and computing gradients etc.
inplace (bool, optional) – Whether to replace the loops inplace.
split_opts – Supplied to tensor_split().

Return type:

loop_simplify_[source]¶

full_simplify(seq='ADCR', output_inds=None, atol=1e-12, equalize_norms=False, cache=None, rank_simplify_opts=None, loop_simplify_opts=None, split_simplify_opts=None, custom_methods=(), split_method='svd', check_zero='auto', inplace=False, progbar=False)[source]¶

Perform a series of tensor network ‘simplifications’ in a loop until there is no more reduction in the number of tensors or indices. Note that apart from rank-reduction, the simplification methods make use of the non-zero structure of the tensors, and thus changes to this will potentially produce different simplifications.

Parameters:

seq (str, optional) –
Which simplifications and which order to perform them in.
- 'A' : stands for antidiag_gauge
- 'D' : stands for diagonal_reduce
- 'C' : stands for column_reduce
- 'R' : stands for rank_simplify
- 'S' : stands for split_simplify
- 'L' : stands for loop_simplify
If you want to keep the tensor network ‘simple’, i.e. with no hyperedges, then don’t use 'D' (moreover 'A' is redundant).
output_inds (sequence of str, optional) – Explicitly set which indices of the tensor network are output indices and thus should not be modified. If not specified the tensor network is assumed to be a ‘standard’ one where indices that only appear once are the output indices.
atol (float, optional) – The absolute tolerance when indentifying zero entries of tensors and performing low-rank decompositions.
equalize_norms (bool or float) – Actively renormalize the tensors during the simplification process. Useful for very large TNs. If True, the norms, in the formed of stripped exponents, will be redistributed at the end. If an actual number, the final tensors will all have this norm, and the scaling factor will be stored as a base-10 exponent in tn.exponent.
cache (None or set) – A persistent cache for each simplification process to mark already processed tensors.
check_zero (bool, optional) – Whether to check if tensors have zero norm and in that case do nothing if and when equalizing norms, rather than generating a NaN. If ‘auto’ this will only be turned on if other methods that explicitly check data entries (“A”, “D”, “C”, “S”, “L”) are being used (the default).
progbar (bool, optional) – Show a live progress bar of the simplification process.
inplace (bool, optional) – Whether to perform the simplification inplace.

Return type:

full_simplify_[source]¶

hyperinds_resolve(mode='dense', sorter=None, output_inds=None, inplace=False)[source]¶

Convert this into a regular tensor network, where all indices appear at most twice, by inserting COPY tensor or tensor networks for each hyper index.

Parameters:

mode ({'dense', 'mps', 'tree'}, optional) – What type of COPY tensor(s) to insert.
sorter (None or callable, optional) – If given, a function to sort the indices that a single hyperindex will be turned into. Th function is called like tids.sort(key=sorter).
inplace (bool, optional) – Whether to insert the COPY tensors inplace.

Return type:

hyperinds_resolve_[source]¶

compress_simplify(output_inds=None, atol=1e-06, simplify_sequence_a='ADCRS', simplify_sequence_b='RPL', hyperind_resolve_mode='tree', hyperind_resolve_sort='clustering', final_resolve=False, split_method='svd', max_simplification_iterations=100, converged_tol=0.01, equalize_norms=True, check_zero=True, progbar=False, inplace=False, **full_simplify_opts)[source]¶

compress_simplify_[source]¶

max_bond()[source]¶: Return the size of the largest bond (i.e. index connecting 2+ tensors) in this network.

property shape¶: Effective, i.e. outer, shape of this TensorNetwork.

property dtype¶: The dtype of this TensorNetwork, this is the minimal common type of all the tensors data.

property dtype_name¶: The name of the data type of the array elements.

property backend¶: Get the backend of any tensor in this network, asssuming it to be the same for all tensors.

iscomplex()[source]¶

astype(dtype, inplace=False)[source]¶: Convert the type of all tensors in this network to dtype.

astype_[source]¶

__getstate__()[source]¶

__setstate__(state)[source]¶

_repr_info()[source]¶: General info to show in various reprs. Sublasses can add more relevant info to this dict.

_repr_info_str()[source]¶: Render the general info as a string.

_repr_html_()[source]¶: Render this TensorNetwork as HTML, for Jupyter notebooks.

__str__()[source]¶

__repr__()[source]¶

draw[source]¶

draw_3d[source]¶

draw_interactive[source]¶

draw_3d_interactive[source]¶

graph[source]¶

visualize_tensors[source]¶

quimb.tensor.bonds(t1: Tensor, t2: Tensor)[source]¶: Getting any indices connecting the Tensor(s) or TensorNetwork(s) t1 and t2.

quimb.tensor.bonds_size(t1: Tensor, t2: Tensor)[source]¶: Get the size of the bonds linking tensors or tensor networks t1 and t2.

quimb.tensor.connect(t1: Tensor, t2: Tensor, ax1, ax2)[source]¶

Connect two tensors by setting a shared index for the specified dimensions. This is an inplace operation that will also affect any tensor networks viewing these tensors.

Parameters:

t1 (Tensor) – The first tensor.
t2 – The second tensor.
ax1 (int) – The dimension (axis) to connect on the first tensor.
ax2 (int) – The dimension (axis) to connect on the second tensor.

Examples

>>> X = rand_tensor([2, 3], inds=['a', 'b'])
>>> Y = rand_tensor([3, 4], inds=['c', 'd'])

>>> tn = (X | Y)  # is *view* of tensors (``&`` would copy them)
>>> print(tn)
TensorNetwork([
    Tensor(shape=(2, 3), inds=('a', 'b'), tags=()),
    Tensor(shape=(3, 4), inds=('c', 'd'), tags=()),
])

>>> connect(X, Y, 1, 0)  # modifies tensors *and* viewing TN
>>> print(tn)
TensorNetwork([
    Tensor(shape=(2, 3), inds=('a', '_e9021e0000002'), tags=()),
    Tensor(shape=(3, 4), inds=('_e9021e0000002', 'd'), tags=()),
])

>>>  tn ^ all
Tensor(shape=(2, 4), inds=('a', 'd'), tags=())

quimb.tensor.group_inds(t1: Tensor, t2: Tensor)[source]¶

Group bonds into left only, shared, and right only. If t1 or t2 are TensorNetwork objects, then only outer indices are considered.

Parameters:

t1 (Tensor or TensorNetwork) – The first tensor or tensor network.
t2 (Tensor or TensorNetwork) – The second tensor or tensor network.

Returns:

left_inds (list[str]) – Indices only in t1.
shared_inds (list[str]) – Indices in both t1 and t2.
right_inds (list[str]) – Indices only in t2.

quimb.tensor.new_bond(T1: Tensor, T2: Tensor, size=1, name=None, axis1=0, axis2=0)[source]¶

Inplace addition of a new bond between tensors T1 and T2. The size of the new bond can be specified, in which case the new array parts will be filled with zeros.

Parameters:

T1 (Tensor) – First tensor to modify.
T2 (Tensor) – Second tensor to modify.
size (int, optional) – Size of the new dimension.
name (str, optional) – Name for the new index.
axis1 (int, optional) – Position on the first tensor for the new dimension.
axis2 (int, optional) – Position on the second tensor for the new dimension.

class quimb.tensor.oset(it=())[source]¶

An ordered set which stores elements as the keys of dict (ordered as of python 3.6). ‘A few times’ slower than using a set directly for small sizes, but makes everything deterministic.

__slots__ = ('_d',)¶

_d¶

classmethod _from_dict(d)[source]¶

classmethod from_dict(d)[source]¶: Public method makes sure to copy incoming dictionary.

copy()[source]¶

__deepcopy__(memo)[source]¶

add(k)[source]¶

discard(k)[source]¶

remove(k)[source]¶

clear()[source]¶

update(*others)[source]¶

union(*others)[source]¶

intersection_update(*others)[source]¶

intersection(*others)[source]¶

difference_update(*others)[source]¶

difference(*others)[source]¶

popleft()[source]¶

popright()[source]¶

pop[source]¶

__eq__(other)[source]¶

__or__(other)[source]¶

__ior__(other)[source]¶

__and__(other)[source]¶

__iand__(other)[source]¶

__sub__(other)[source]¶

__isub__(other)[source]¶

__len__()[source]¶

__iter__()[source]¶

__contains__(x)[source]¶

__repr__()[source]¶

quimb.tensor.rand_uuid(base='')[source]¶

Return a guaranteed unique, shortish identifier, optional appended to base.

Examples

>>> rand_uuid()
'_2e1dae1b'

>>> rand_uuid('virt-bond')
'virt-bond_bf342e68'

quimb.tensor.tensor_balance_bond(t1: Tensor, t2: Tensor, smudge=1e-06)[source]¶

Gauge the bond between two tensors such that the norm of the ‘columns’ of the tensors on each side is the same for each index of the bond.

Parameters:

t1 (Tensor) – The first tensor, should share a single index with t2.
t2 (Tensor) – The second tensor, should share a single index with t1.
smudge (float, optional) – Avoid numerical issues by ‘smudging’ the correctional factor by this much - the gauging introduced is still exact.

quimb.tensor.tensor_canonize_bond(T1: Tensor, T2: Tensor, absorb='right', gauges=None, gauge_smudge=1e-06, **split_opts)[source]¶

Inplace ‘canonization’ of two tensors. This gauges the bond between the two such that T1 is isometric:

  |   |          |   |          |   |
--1---2--  =>  -->~R-2--  =>  -->~~~O--
  |   |          |   |          |   |
  .                ...
 <QR>              contract

Parameters:

T1 (Tensor) – The tensor to be isometrized.
T2 (Tensor) – The tensor to absorb the R-factor into.
absorb ({'right', 'left', 'both', None}, optional) – Which tensor to effectively absorb the singular values into.
split_opts – Supplied to tensor_split(), with modified defaults of method=='qr' and absorb='right'.

quimb.tensor.tensor_compress_bond(T1: Tensor, T2: Tensor, reduced=True, absorb='both', gauges=None, gauge_smudge=1e-06, info=None, **compress_opts)[source]¶

Inplace compress between the two single tensors. It follows the following steps (by default) to minimize the size of SVD performed:

a)│   │        b)│        │        c)│       │
━━●━━━●━━  ->  ━━>━━○━━○━━<━━  ->  ━━>━━━M━━━<━━
  │   │          │  ....  │          │       │
 <*> <*>          contract              <*>
 QR   LQ            -><-                SVD

          d)│            │        e)│   │
      ->  ━━>━━━ML──MR━━━<━━  ->  ━━●───●━━
            │....    ....│          │   │
          contract  contract          ^compressed bond
            -><-      -><-

Parameters:

T1 (Tensor) – The left tensor.
T2 (Tensor) – The right tensor.
max_bond (int or None, optional) – The maxmimum bond dimension.
cutoff (float, optional) – The singular value cutoff to use.
reduced ({True, False, "left", "right"}, optional) – Whether to perform the QR reduction as above or not. If False, contract both tensors together and perform a single SVD. If ‘left’ or ‘right’ then just perform the svd on the left or right tensor respectively. This can still be optimal if the other tensor is already isometric, i.e. the pair are right or left canonical respectively.
absorb ({'both', 'left', 'right', None}, optional) – Where to absorb the singular values after decomposition.
info (None or dict, optional) – A dict for returning extra information such as the singular values.
compress_opts – Supplied to tensor_split().

quimb.tensor.tensor_contract(*tensors: Tensor, output_inds=None, optimize=None, get=None, backend=None, preserve_tensor=False, drop_tags=False, strip_exponent=False, exponent=None, **contract_opts)[source]¶

Contract a collection of tensors into a scalar or tensor, automatically aligning their indices and computing an optimized contraction path. The output tensor will have the union of tags from the input tensors.

Parameters:

tensors (sequence of Tensor) – The tensors to contract.
output_inds (sequence of str) – The output indices. These can be inferred if the contraction has no ‘hyper’ indices, in which case the output indices are those that appear only once in the input indices, and ordered as they appear in the inputs. For hyper indices or a specific ordering, these must be supplied.
optimize (str, PathOptimizer, ContractionTree or path_like, optional) –
The contraction path optimization strategy to use.
- None: use the default strategy,
- str: use the preset strategy with the given name,
- cotengra.HyperOptimizer: find the contraction using this optimizer, supports slicing,
- opt_einsum.PathOptimizer: find the path using this optimizer.
- cotengra.ContractionTree: use this exact tree, supports slicing,
- path_like: use this exact path.
Contraction with cotengra might be a bit more efficient but the main reason would be to handle sliced contraction automatically, as well as the fact that it uses autoray internally.
get (str, optional) –
What to return. If:
- None (the default) - return the resulting scalar or Tensor.
- 'expression' - return a callbable expression that performs the contraction and operates on the raw arrays.
- 'tree' - return the cotengra.ContractionTree describing the contraction.
- 'path' - return the raw ‘path’ as a list of tuples.
- 'symbol-map' - return the dict mapping indices to ‘symbols’ (single unicode letters) used internally by cotengra
- 'path-info' - return the opt_einsum.PathInfo path object with detailed information such as flop cost. The symbol-map is also added to the quimb_symbol_map attribute.
backend ({'auto', 'numpy', 'jax', 'cupy', 'tensorflow', ...}, optional) – Which backend to use to perform the contraction. Supplied to cotengra.
preserve_tensor (bool, optional) – Whether to return a tensor regardless of whether the output object is a scalar (has no indices) or not.
drop_tags (bool, optional) – Whether to drop all tags from the output tensor. By default the output tensor will keep the union of all tags from the input tensors.
strip_exponent (bool, optional) – If True, return the exponent of the result, log10, as well as the rescaled ‘mantissa’. Useful for very large or small values.
exponent (float, optional) – If supplied, an overall base exponent to scale the result by.
contract_opts – Passed to cotengra.array_contract.

Return type:

scalar or Tensor

quimb.tensor.tensor_direct_product(T1: Tensor, T2: Tensor, sum_inds=(), inplace=False)[source]¶

Direct product of two Tensors. Any axes included in sum_inds must be the same size and will be summed over rather than concatenated. Summing over contractions of TensorNetworks equates to contracting a TensorNetwork made of direct products of each set of tensors. I.e. (a1 @ b1) + (a2 @ b2) == (a1 (+) a2) @ (b1 (+) b2).

Parameters:

T1 (Tensor) – The first tensor.
T2 (Tensor) – The second tensor, with matching indices and dimensions to T1.
sum_inds (sequence of str, optional) – Axes to sum over rather than combine, e.g. physical indices when adding tensor networks.
inplace (bool, optional) – Whether to modify T1 inplace.

Returns:

Like T1, but with each dimension doubled in size if not in sum_inds.

Return type:

quimb.tensor.tensor_fuse_squeeze(t1: Tensor, t2: Tensor, squeeze=True, gauges=None)[source]¶: If t1 and t2 share more than one bond fuse it, and if the size of the shared dimenion(s) is 1, squeeze it. Inplace operation.

quimb.tensor.tensor_network_distance(tnA, tnB, xAA=None, xAB=None, xBB=None, method='auto', normalized=False, output_inds=None, **contract_opts)[source]¶

Compute the Frobenius norm distance between two tensor networks:

\[D(A, B) = | A - B |_{\mathrm{fro}} = \mathrm{Tr} [(A - B)^{\dagger}(A - B)]^{1/2} = ( \langle A | A \rangle - 2 \mathrm{Re} \langle A | B \rangle| + \langle B | B \rangle ) ^{1/2}\]

which should have matching outer indices. Note the default approach to computing the norm is precision limited to about eps**0.5 where eps is the precision of the data type, e.g. 1e-8 for float64. This is due to the subtraction in the above expression.

Parameters:

tnA (TensorNetwork or Tensor) – The first tensor network operator.
tnB (TensorNetwork or Tensor) – The second tensor network operator.
xAA (None or scalar) – The value of A.H @ A if you already know it (or it doesn’t matter).
xAB (None or scalar) – The value of A.H @ B if you already know it (or it doesn’t matter).
xBB (None or scalar) – The value of B.H @ B if you already know it (or it doesn’t matter).
method ({'auto', 'overlap', 'dense'}, optional) – How to compute the distance. If 'overlap', the default, the distance will be computed as the sum of overlaps, without explicitly forming the dense operators. If 'dense', the operators will be directly formed and the norm computed, which can be quicker when the exterior dimensions are small. If 'auto', the dense method will be used if the total operator (outer) size is <= 2**16.
normalized (bool or str, optional) – If True, then normalize the distance by the norm of the two operators, i.e. D(A, B) * 2 / (|A| + |B|). The resulting distance lies between 0 and 2 and is more useful for assessing convergence. If 'infidelity', compute the normalized infidelity 1 - |<A|B>|^2 / (|A| |B|), which can be faster to optimize e.g., but does not take into account normalization.
output_inds (sequence of str, optional) – Specify the output indices of tnA and tnB to contract over. This can be necessary if either network has hyper indices.
contract_opts – Supplied to contract().

Returns:

Return type:

quimb.tensor.tensor_network_fit_als(tn, tn_target, tags=None, steps=100, tol=1e-09, dense_solve='auto', solver='auto', solver_maxiter=4, solver_dense='auto', enforce_pos=False, pos_smudge=None, tnAA=None, tnAB=None, xBB=None, output_inds=None, contract_optimize='auto-hq', inplace=False, progbar=False, **kwargs)[source]¶

Optimize the fit of tn with respect to tn_target using alternating least squares (ALS). This minimizes the norm of the difference between the two tensor networks, which must have matching outer indices, using overlaps.

Parameters:

tn (TensorNetwork) – The tensor network to fit.
tn_target (TensorNetwork) – The target tensor network to fit tn to.
tags (sequence of str, optional) – If supplied, only optimize tensors matching any of given tags.
steps (int, optional) – The maximum number of ALS steps.
tol (float, optional) – The target norm distance.
dense_solve ({'auto', True, False}, optional) – Whether to solve the local minimization problem in dense form. If 'auto', will only use dense form for small tensors.
solver ({"auto", None, "cg", ...}, optional) – What solver to use for the iterative (but not dense) local minimization. If None will use a built in conjugate gradient solver. If a string, will use the corresponding solver from scipy.sparse.linalg.
solver_maxiter (int, optional) – The maximum number of iterations for the iterative solver.
solver_dense ({"auto", None, 'solve', 'eigh', 'lstsq', ...}, optional) – The underlying driver function used to solve the local minimization, e.g. numpy.linalg.solve for 'solve' with numpy backend, if solving the local problem in dense form.
enforce_pos (bool, optional) – Whether to enforce positivity of the locally formed environments, which can be more stable, only for dense solves. This sets solver_dense='eigh'.
pos_smudge (float, optional) – If enforcing positivity, the level below which to clip eigenvalues for make the local environment positive definit, only for dense solves.
tnAA (TensorNetwork, optional) – If you have already formed the overlap tn.H & tn, maybe approximately, you can supply it here. The unconjugated layer should have tag '__KET__' and the conjugated layer '__BRA__'. Each tensor being optimized should have tag '__VAR{i}__'.
tnAB (TensorNetwork, optional) – If you have already formed the overlap tn_target.H & tn, maybe approximately, you can supply it here. Each tensor being optimized should have tag '__VAR{i}__'.
xBB (float, optional) – If you have already know, have computed tn_target.H @ tn_target, or it doesn’t matter, you can supply the value here.
contract_optimize (str, optional) – The contraction path optimized used to contract the local environments. Note 'greedy' is the default in order to maximize shared work.
inplace (bool, optional) – Update tn in place.
progbar (bool, optional) – Show a live progress bar of the fitting process.

Return type:

quimb.tensor.tensor_network_fit_autodiff(tn, tn_target, steps=1000, tol=1e-09, autodiff_backend='autograd', contract_optimize='auto-hq', distance_method='auto', normalized='squared', output_inds=None, xBB=None, inplace=False, progbar=False, **kwargs)[source]¶

Optimize the fit of tn with respect to tn_target using automatic differentation. This minimizes the norm of the difference between the two tensor networks, which must have matching outer indices, using overlaps.

Parameters:

tn (TensorNetwork) – The tensor network to fit.
tn_target (TensorNetwork) – The target tensor network to fit tn to.
steps (int, optional) – The maximum number of autodiff steps.
tol (float, optional) – The target norm distance.
autodiff_backend (str, optional) – Which backend library to use to perform the gradient computation.
contract_optimize (str, optional) – The contraction path optimized used to contract the overlaps.
distance_method ({'auto', 'dense', 'overlap'}, optional) – Supplied to tensor_network_distance(), controls how the distance is computed.
normalized (bool or str, optional) – If True, then normalize the distance by the norm of the two operators, i.e. D(A, B) * 2 / (|A| + |B|). The resulting distance lies between 0 and 2 and is more useful for assessing convergence. If 'infidelity', compute the normalized infidelity 1 - |<A|B>|^2 / (|A| |B|), which can be faster to optimize e.g., but does not take into account normalization.
output_inds (sequence of str, optional) – Specify the output indices of tnA and tnB to contract over. This can be necessary if either network has hyper indices.
xBB (float, optional) – If you already know, have computed tn_target.H @ tn_target, or don’t care about the overall scale of the norm distance, you can supply a value here.
inplace (bool, optional) – Update tn in place.
progbar (bool, optional) – Show a live progress bar of the fitting process.
kwargs – Passed to TNOptimizer.

quimb.tensor.tensor_network_gate_inds(self: TensorNetwork, G, inds, contract=False, tags=None, info=None, inplace=False, **compress_opts)[source]¶

Apply the ‘gate’ G to indices inds, propagating them to the outside, as if applying G @ x.

Parameters:

G (array_ike) – The gate array to apply, should match or be factorable into the shape (*phys_dims, *phys_dims).
inds (str or sequence or str,) – The index or indices to apply the gate to.
contract ({False, True, 'split', 'reduce-split', 'split-gate',) –
‘swap-split-gate’, ‘auto-split-gate’}, optional How to apply the gate:
- False: gate is added to network lazily and nothing is contracted, tensor network structure is thus not maintained.
- True: gate is contracted eagerly with all tensors involved, tensor network structure is thus only maintained if gate acts on a single site only.
- ’split’: contract all involved tensors then split the result back into two.
- ’reduce-split’: factor the two physical indices into ‘R-factors’ using QR decompositions on the original site tensors, then contract the gate, split it and reabsorb each side. Much cheaper than 'split'.
- ’split-gate’: lazily add the gate as with False, but split the gate tensor spatially.
- ’swap-split-gate’: lazily add the gate as with False, but split the gate as if an extra SWAP has been applied.
- ’auto-split-gate’: lazily add the gate as with False, but maybe apply one of the above options depending on whether they result in a rank reduction.
The named methods are relevant for two site gates only, for single site gates they use the contract=True option which also maintains the structure of the TN. See below for a pictorial description of each method.
tags (str or sequence of str, optional) – Tags to add to the new gate tensor.
info (None or dict, optional) – Used to store extra optional information such as the singular values if not absorbed.
inplace (bool, optional) – Whether to perform the gate operation inplace on the tensor network or not.
compress_opts – Supplied to tensor_split() for any contract methods that involve splitting. Ignored otherwise.

Returns:

G_tn

Return type:

Notes

The contract options look like the following (for two site gates).

contract=False:

  .   .  <- inds
  │   │
  GGGGG
  │╱  │╱
──●───●──
 ╱   ╱

contract=True:

  │╱  │╱
──GGGGG──
 ╱   ╱

contract='split':

  │╱  │╱          │╱  │╱
──GGGGG──  ==>  ──G┄┄┄G──
 ╱   ╱           ╱   ╱
  <SVD>

contract='reduce-split':

  │   │             │ │
  GGGGG             GGG               │ │
  │╱  │╱   ==>     ╱│ │  ╱   ==>     ╱│ │  ╱          │╱  │╱
──●───●──       ──>─●─●─<──       ──>─GGG─<──  ==>  ──G┄┄┄G──
 ╱   ╱           ╱     ╱           ╱     ╱           ╱   ╱
<QR> <LQ>                            <SVD>

For one site gates when one of these ‘split’ methods is supplied contract=True is assumed.

contract='split-gate':

  │   │ <SVD>
  G~~~G
  │╱  │╱
──●───●──
 ╱   ╱

contract='swap-split-gate':

   ╲ ╱
    ╳
   ╱ ╲ <SVD>
  G~~~G
  │╱  │╱
──●───●──
 ╱   ╱

contract='auto-split-gate' chooses between the above two and False, depending on whether either results in a lower rank.

quimb.tensor.tensor_network_sum(tnA: TensorNetwork, tnB: TensorNetwork, inplace=False)[source]¶

Sum of two tensor networks, whose indices should match exactly, using direct products.

Parameters:

tnA (TensorNetwork) – The first tensor network.
tnB (TensorNetwork) – The second tensor network.

Returns:

The sum of tnA and tnB, with increased bond dimensions.

Return type: