quimb.tensor.circuit.peps¶
PEPS simple-update circuit simulator.
Classes¶
Quantum circuit simulation keeping the state as a generic tensor |
Module Contents¶
- class quimb.tensor.circuit.peps.CircuitPEPSSimpleUpdate(N=None, *, edges=None, gates=None, psi0=None, max_bond=None, cutoff=1e-10, renorm=False, gauge_smudge=1e-12, equilibrate_every=None, equilibrate_opts=None, gate_opts=None, dtype=None, to_backend=None, convert_eager=True, **circuit_opts)[source]¶
Bases:
quimb.tensor.circuit.simple_update.CircuitSimpleUpdateQuantum circuit simulation keeping the state as a generic tensor network (a “PEPS” defined by an arbitrary graph of
edges) and applying gates with the simple update rule. The state always keeps a single tensor per site, with bonds only along the supplied edges; two-qubit gates are only supported on those edges. Bond singular values are tracked as Vidal-style gauges, which makes gate application and the computation of local expectations cheap and approximate.This is useful for circuits on lattices that build up more than 1D worth of entanglement, where an exact or MPS simulation is intractable but a truncated, gauged tensor network state is a good approximation.
- Parameters:
N (int, optional) – The number of qubits in the circuit. If not given it is inferred from the geometry. Supply it to pad the geometry up to
Nsites, including any that have no edges.edges (sequence[tuple[int, int]], optional) – The edges defining the geometry of the PEPS. A bond is placed between each pair of sites, and two-qubit gates are only supported on these edges. Every site appearing in
edgesis included. If not given the geometry is taken fromgatesorpsi0instead.gates (sequence, optional) – If
edgesis not given, infer the geometry from the two-qubit gates in this sequence. The gates are only inspected here, not applied, so you still pass them toapply_gates()afterwards.psi0 (TensorNetworkGenVector, optional) – Supply the initial state directly instead of starting from the
|00...0>product state. Ifedgesis not given the geometry is read from the bonds of this state, and the bond gauges are seeded from it. Only a single seeding sweep is performed; unlike imaginary time simple update the gauge matters immediately, so for an arbitrarypsi0you may want to callequilibrate()once before applying gates.max_bond (int, optional) – The maximum bond dimension to truncate to when applying gates.
cutoff (float, optional) – The singular value cutoff to use when truncating after applying gates.
renorm (bool, optional) – Whether to renormalize the singular values of a bond after each gate. The default
Falsetracks the norm of the state rather than forcing it to one, which is the sensible choice for real time and general circuit dynamics. SetTrueto instead keep the state normalized after every gate, e.g. for the near-identity gates of imaginary time evolution.gauge_smudge (float, optional) – Small value added to the gauges before they are multiplied in and inverted, for numerical stability with very small singular values.
equilibrate_every (int, optional) – If given, automatically call
equilibrate()after every this many gates have been applied.equilibrate_opts (dict, optional) – Default options forwarded to
equilibrate().gate_opts (dict, optional) – Default options to pass to
gate_simple_such asmax_bondandcutoff.dtype (str, optional) – If given, ensure the state tensors are cast to this data type.
to_backend (callable, optional) – If given, apply this function to the state tensors to convert them to a particular array backend.
convert_eager (bool, optional) – Whether to apply the
dtypeandto_backendconversions eagerly as each gate is applied. The defaultTruematches the other running simulators (e.g.CircuitMPS), since the simple update rule contracts each gate into the state immediately rather than building a lazy network to contract later.
- gauges¶
The current Vidal-style bond gauges (singular values), keyed by bond index, updated in place as gates are applied.
Notes
The gates applied must address qubits using the same labels that appear in
edges. Two-qubit gates are only supported along an existing edge.Examples
>>> import quimb.tensor as qtn >>> edges = [(0, 1), (1, 2), (0, 3), (1, 4), (2, 5), (3, 4), (4, 5)] >>> circ = qtn.CircuitPEPSSimpleUpdate(edges=edges, max_bond=8) >>> circ.apply_gates(gates) >>> peps = circ.psi
See also
CircuitMPS,CircuitDense- gauges¶
- _equilibrate_every = None¶
- _equilibrate_opts¶
- copy()[source]¶
Copy the circuit, including its state, gauges and geometry. The base
CircuitSimpleUpdatecopy carries the geometry; the gauges and equilibrate options are copied here so the two circuits can be evolved independently.
- _apply_gate(gate, tags=None, **gate_opts)[source]¶
Apply a
Gateto thisCircuit. This is the main method that all calls to apply a gate should go through.
- 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().
- equilibrate(**gauge_opts)[source]¶
Re-gauge the whole state with the simple update rule, improving the consistency of the tracked bond gauges. This does not change the state represented, only the gauge, and can be called periodically between rounds of gates to keep the simple update approximation well behaved.
The default options given at construction via
equilibrate_optsare applied first, with any keyword arguments here taking precedence.- Parameters:
gauge_opts – Supplied to
gauge_all_simple_(), for examplemax_iterationsandtol.
- local_expectation(G, where, *, max_distance=0, normalized=True, **contract_opts)[source]¶
Compute the local expectation value of operator
Gat the site(s)where, using the simple update bond gauges to approximate the environment beyondmax_distance.- Parameters:
G (array_like) – The local operator.
where (hashable or sequence[hashable]) – The site or sites to compute the expectation at. A single site label (which may itself be a tuple, e.g. a 2D coordinate) is detected by membership in the set of sites.
max_distance (int, optional) – How many graph hops of neighboring tensors to include in the local cluster used to approximate the reduced density matrix. The default
0uses only the target site(s) and their gauges, matchingcompute_local_expectation_cluster().normalized (bool, optional) – Whether to normalize by the local norm.
contract_opts – Supplied to
compute_local_expectation_cluster().
- Return type:
- get_state(absorb_gauges=True)[source]¶
Return the current PEPS state, optionally absorbing the bond gauges.
- Parameters:
absorb_gauges (bool or "return", optional) – How to handle the tracked Vidal-style bond gauges. If
True(the default) the gauges are absorbed, so the returned tensor network is the actual wavefunction (up to the simple update approximation). IfFalsethe gauges are added to the network as uncontracted diagonal tensors. If"return"the raw gauged network and a copy of the gauges are returned separately. The internal state is left untouched in every case.- Returns:
psi (TensorNetwork) – The current state.
gauges (dict) – The current gauges, only if
absorb_gauges == "return".
- get_psi()[source]¶
Get the PEPS tensor network state, with the simple update bond gauges absorbed back in so that it represents the actual wavefunction (a proper contraction of it gives the state, up to the simple update approximation). The internal gauged form is left untouched. Shorthand for
get_state(absorb_gauges=True).
- to_dense(*args, **kwargs)[source]¶
Contract the gauged PEPS into a dense wavefunction, a column-vector
qarrayof length2**Nordered likesites, matching the output ofCircuit.to_dense(). This is the actual (approximate) state, so the cost grows exponentially with the number of qubits.Arguments are forwarded to
to_dense().