quimb.tensor.tensor_1d_compress

Generic methods for compressing 1D-like tensor networks, where the tensor network can locally have arbitrary structure and outer indices.

  • [x] the direct method

  • [x] the density matrix method

  • [x] the zip-up method

  • [x] the zip-up first method

  • [x] the 1-site variational fit method, including sums of tensor networks

  • [x] the 2-site variational fit method, including sums of tensor networks

  • [x] the local projector method (CTMRG and HOTRG style)

  • [x] the autofit method (via non-1d specific ALS or autodiff)

Module Contents

Functions

enforce_1d_like(tn[, site_tags, fix_bonds, inplace])

Check that tn is 1D-like with OBC, i.e. 1) that each tensor has

possibly_permute_(tn, permute_arrays)

tensor_network_1d_compress_direct(tn[, max_bond, ...])

Compress a 1D-like tensor network using the 'direct' or 'naive' method,

tensor_network_1d_compress_dm(tn[, max_bond, cutoff, ...])

Compress any 1D-like tensor network using the 'density matrix' method

tensor_network_1d_compress_zipup(tn[, max_bond, ...])

Compress a 1D-like tensor network using the 'zip-up' algorithm due to

tensor_network_1d_compress_zipup_first(tn[, max_bond, ...])

Compress this 1D-like tensor network using the 'zip-up first' algorithm,

_tn1d_fit_sum_sweep_1site(tn_fit, tn_overlaps, site_tags)

Core sweep of the 1-site 1D fit algorithm.

_tn1d_fit_sum_sweep_2site(tn_fit, tn_overlaps, site_tags)

Core sweep of the 2-site 1D fit algorithm.

tensor_network_1d_compress_fit(tns[, max_bond, ...])

Compress any 1D-like (can have multiple tensors per site) tensor network

tensor_network_1d_compress(tn[, max_bond, cutoff, ...])

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

mps_gate_with_mpo_lazy(mps, mpo[, inplace])

Apply an MPO to an MPS lazily, i.e. nothing is contracted, but the new

mps_gate_with_mpo_direct(mps, mpo[, max_bond, cutoff, ...])

Apply an MPO to an MPS using the boundary compression method, that is,

mps_gate_with_mpo_dm(mps, mpo[, max_bond, cutoff, inplace])

Gate this MPS with an MPO, using the density matrix compression method.

mps_gate_with_mpo_zipup(mps, mpo[, max_bond, cutoff, ...])

Apply an MPO to an MPS using the 'zip-up' algorithm due to

mps_gate_with_mpo_zipup_first(mps, mpo[, max_bond, ...])

Apply an MPO to an MPS by first using the zip-up method with a larger

mps_gate_with_mpo_fit(mps, mpo, max_bond, **kwargs)

Gate an MPS with an MPO using the variational fitting or DMRG-style

mps_gate_with_mpo_autofit(self, mpo, max_bond[, ...])

Fit a MPS to a MPO applied to an MPS using geometry generic versions

mps_gate_with_mpo_projector(self, mpo, max_bond[, ...])

Apply an MPO to an MPS using local projectors, in the style of CTMRG

Attributes

_TN1D_COMPRESS_METHODS

quimb.tensor.tensor_1d_compress.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_1d_compress.possibly_permute_(tn, permute_arrays)[source]
quimb.tensor.tensor_1d_compress.tensor_network_1d_compress_direct(tn, max_bond=None, cutoff=1e-10, site_tags=None, normalize=False, canonize=True, cutoff_mode='rsum2', permute_arrays=True, optimize='auto-hq', sweep_reverse=False, equalize_norms=False, inplace=False, **compress_opts)[source]

Compress a 1D-like tensor network using the ‘direct’ or ‘naive’ method, that is, explicitly contracting site-wise to form a MPS-like TN, canonicalizing in one direction, then compressing in the other. This has the same scaling as the density matrix (dm) method, but a larger prefactor. It can still be faster for small bond dimensions however, and is potentially higher precision since it works in the space of singular values directly rather than singular values squared. It is not quite optimal in terms of error due to the compounding errors of the SVDs.

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.

  • 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.

  • normalize (bool, optional) – Whether to normalize the final tensor network, making use of the fact that the output tensor network is in right canonical form.

  • canonize (bool, optional) – Whether to canonicalize the network in one direction before compressing in the other.

  • cutoff_mode ({"rsum2", "rel", ...}, optional) – The mode to use when truncating the singular values of the decomposed tensors. See tensor_split().

  • 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.

  • equalize_norms (bool, optional) – Whether to renormalize the tensors during the compression procedure. If True the gathered exponent will be redistributed equally among the tensors. If a float, all tensors will be renormalized to this value, and the gathered exponent is tracked in tn.exponent of the returned tensor network.

  • inplace (bool, optional) – Whether to perform the compression inplace or not.

  • compress_opts – Supplied to tensor_split().

Returns:

The compressed tensor network, with canonical center at site_tags[0] (‘right canonical’ form) or site_tags[-1] (‘left canonical’ form) if sweep_reverse.

Return type:

TensorNetwork

quimb.tensor.tensor_1d_compress.tensor_network_1d_compress_dm(tn, max_bond=None, cutoff=1e-10, site_tags=None, normalize=False, cutoff_mode='rsum1', permute_arrays=True, optimize='auto-hq', sweep_reverse=False, canonize=True, equalize_norms=False, inplace=False, **compress_opts)[source]

Compress any 1D-like tensor network using the ‘density matrix’ method (https://tensornetwork.org/mps/algorithms/denmat_mpo_mps/).

While this has the same scaling as the direct method, in practice it can often be faster, especially at large bond dimensions. Potentially there are some situations where the direct method is more stable with regard to precision, since the density matrix method works in the ‘squared’ picture.

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) – The truncation error to use when compressing the double layer tensor network.

  • 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.

  • normalize (bool, optional) – Whether to normalize the final tensor network, making use of the fact that the output tensor network is in right canonical form.

  • cutoff_mode ({"rsum1", "rel", ...}, optional) – The mode to use when truncating the singular values of the decomposed tensors. See tensor_split(). Note for the density matrix method the default ‘rsum1’ mode acts like ‘rsum2’ for the direct method due to truncating in the squared space.

  • 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.

  • canonize (bool, optional) – Dummy argument to match the signature of other compression methods.

  • 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 or not.

  • compress_opts – Supplied to tensor_split().

Returns:

The compressed tensor network, with canonical center at site_tags[0] (‘right canonical’ form) or site_tags[-1] (‘left canonical’ form) if sweep_reverse.

Return type:

TensorNetwork

quimb.tensor.tensor_1d_compress.tensor_network_1d_compress_zipup(tn, max_bond=None, cutoff=1e-10, site_tags=None, canonize=True, normalize=False, cutoff_mode='rsum2', permute_arrays=True, optimize='auto-hq', sweep_reverse=False, equalize_norms=False, inplace=False, **compress_opts)[source]

Compress a 1D-like tensor network using the ‘zip-up’ algorithm due to ‘Minimally Entangled Typical Thermal State Algorithms’, E.M. Stoudenmire & Steven R. White (https://arxiv.org/abs/1002.1305). The returned tensor network will have one tensor per site, in the order given by site_tags, with canonical center at site_tags[0] (‘right’ canonical form).

The zipup algorithm scales better than the direct and density matrix methods when multiple tensors are present at each site (such as MPO-MPS multiplication), but is less accurate due to the compressions taking place in an only pseudo-canonical gauge. It generally also only makes sense in the fixed bond dimension case, as opposed to relying on a specific cutoff only.

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.

  • 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 pseudo canonicalize the initial tensor network.

  • normalize (bool, optional) – Whether to normalize the final tensor network, making use of the fact that the output tensor network is in right canonical form.

  • cutoff_mode ({"rsum2", "rel", ...}, optional) – The mode to use when truncating the singular values of the decomposed tensors. See tensor_split().

  • 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.

  • 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 or not.

  • compress_opts – Supplied to tensor_split().

Returns:

The compressed tensor network, with canonical center at site_tags[0] (‘right canonical’ form) or site_tags[-1] (‘left canonical’ form) if sweep_reverse.

Return type:

TensorNetwork

quimb.tensor.tensor_1d_compress.tensor_network_1d_compress_zipup_first(tn, max_bond=None, max_bond_zipup=None, cutoff=1e-10, cutoff_zipup=None, site_tags=None, canonize=True, normalize=False, cutoff_mode='rsum2', permute_arrays=True, optimize='auto-hq', sweep_reverse=False, equalize_norms=False, inplace=False, **compress_opts)[source]

Compress this 1D-like tensor network using the ‘zip-up first’ algorithm, that is, first compressing the tensor network to a larger bond dimension using the ‘zip-up’ algorithm, then compressing to the desired bond dimension using a direct sweep.

Depending on the value of max_bond and max_bond_zipup, this can be scale better than the direct and density matrix methods, but reach close to the same accuracy. As with the ‘zip-up’ method, there is no advantage unless there are multiple tensors per site, and it generally only makes sense in the fixed bond dimension case, as opposed to relying on a specific cutoff only.

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 final maximum bond dimension to compress to.

  • max_bond_zipup (int, optional) – The intermediate maximum bond dimension to compress to using the ‘zip-up’ algorithm. If not given and max_bond is, this is set as twice the target bond dimension, 2 * max_bond.

  • cutoff (float, optional) – A dynamic threshold for discarding singular values when compressing.

  • cutoff_zipup (float, optional) – A dynamic threshold for discarding singular values when compressing to the intermediate bond dimension using the ‘zip-up’ algorithm. If not given, this is set to the same as cutoff if a maximum bond is given, else cutoff / 10.

  • 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 pseudo canonicalize the initial tensor network.

  • normalize (bool, optional) – Whether to normalize the final tensor network, making use of the fact that the output tensor network is in right canonical form.

  • cutoff_mode ({"rsum2", "rel", ...}, optional) – The mode to use when truncating the singular values of the decomposed tensors. See tensor_split().

  • 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.

  • 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 or not.

  • compress_opts – Supplied to tensor_split().

Returns:

The compressed tensor network, with canonical center at site_tags[0] (‘right canonical’ form) or site_tags[-1] (‘left canonical’ form) if sweep_reverse.

Return type:

TensorNetwork

quimb.tensor.tensor_1d_compress._tn1d_fit_sum_sweep_1site(tn_fit, tn_overlaps, site_tags, max_bond=None, cutoff=0.0, envs=None, prepare=True, reverse=False, compute_tdiff=True, optimize='auto-hq')[source]

Core sweep of the 1-site 1D fit algorithm.

quimb.tensor.tensor_1d_compress._tn1d_fit_sum_sweep_2site(tn_fit, tn_overlaps, site_tags, max_bond=None, cutoff=1e-10, envs=None, prepare=True, reverse=False, optimize='auto-hq', compute_tdiff=True, **compress_opts)[source]

Core sweep of the 2-site 1D fit algorithm.

quimb.tensor.tensor_1d_compress.tensor_network_1d_compress_fit(tns, max_bond=None, cutoff=None, tn_fit=None, bsz='auto', initial_bond_dim=8, max_iterations=10, tol=0.0, site_tags=None, cutoff_mode='rsum2', sweep_sequence='RL', normalize=False, permute_arrays=True, optimize='auto-hq', canonize=True, sweep_reverse=False, equalize_norms=False, inplace_fit=False, inplace=False, progbar=False, **compress_opts)[source]

Compress any 1D-like (can have multiple tensors per site) tensor network or sum of tensor networks to an exactly 1D (one tensor per site) tensor network of bond dimension max_bond using the 1-site or 2-site variational fitting (or ‘DMRG-style’) method. The tensor network(s) can have arbitrary inner and outer structure.

This method has the lowest scaling of the standard 1D compression methods and can also provide the most accurate compression, but the actual speed and accuracy depend on the number of iterations required and initial guess, making it a more ‘hands-on’ method.

It’s also the only method to support fitting to a sum of tensor networks directly, rather than having to forming the explicitly summed TN first.

Parameters:

  • tns (TensorNetwork or Sequence[TensorNetwork]) – The tensor network or tensor networks to compress. Each tensor network should have the same outer index structure, and within each tensor network every tensor should have exactly one of the site tags.

  • max_bond (int) – The maximum bond dimension to compress to. If not given, this is set as the maximum bond dimension of the initial guess tensor network, if any, else infinite for bsz=2.

  • cutoff (float, optional) – A dynamic threshold for discarding singular values when compressing. This is only relevant for the 2-site sweeping algorithm (bsz=2), where it defaults to 1e-10.

  • tn_fit (TensorNetwork, dict, or str, optional) – An initial guess for the compressed tensor network. It should matching outer indices and site tags with tn. If a dict, this is assumed to be options to supply to tensor_network_1d_compress to construct the initial guess, inheriting various defaults like initial_bond_dim. If a string, e.g. "zipup", this is shorthand for that compression method with default settings. If not given, a random 1D tensor network will be used.

  • bsz ({"auto", 1, 2}, optional) – The size of the block to optimize while sweeping. If "auto", this will be inferred from the value of max_bond and cutoff.

  • initial_bond_dim (int, optional) – The initial bond dimension to use when creating the initial guess. This is only relevant if tn_fit is not given. For each sweep the allowed bond dimension is doubled, up to max_bond. For 1-site this occurs via explicit bond expansion, while for 2-site it occurs during the 2-site tensor decomposition.

  • max_iterations (int, optional) – The maximum number of variational sweeps to perform.

  • tol (float, optional) – The convergence tolerance, in terms of local tensor distance normalized. If zero, there will be exactly max_iterations sweeps.

  • 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.

  • cutoff_mode ({"rsum2", "rel", ...}, optional) – The mode to use when truncating the singular values of the decomposed tensors. See tensor_split(), if using the 2-site sweeping algorithm.

  • sweep_sequence (str, optional) – The sequence of sweeps to perform, e.g. "LR" means first sweep left to right, then right to left. The sequence is cycled.

  • normalize (bool, optional) – Whether to normalize the final tensor network, making use of the fact that the output tensor network is in left or right canonical form.

  • 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.

  • canonize (bool, optional) – Dummy argument to match the signature of other compression methods.

  • sweep_reverse (bool, optional) – Whether to sweep in the reverse direction, swapping whether the final tensor network is in right or left canonical form, which 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_fit (bool, optional) – Whether to perform the compression inplace on the initial guess tensor network, tn_fit, if supplied.

  • inplace (bool, optional) – Whether to perform the compression inplace on the target tensor network supplied, or tns[0] if a sequence to sum is supplied.

  • progbar (bool, optional) – Whether to show a progress bar. Note the progress bar shows the maximum change of any single tensor norm, not the global change in norm or truncation error.

  • compress_opts – Supplied to tensor_split(), if using the 2-site sweeping algorithm.

Returns:

The compressed tensor network. Depending on sweep_reverse and the last sweep direction, the canonical center will be at either L: site_tags[0] or R: site_tags[-1], or the opposite if sweep_reverse.

Return type:

TensorNetwork

quimb.tensor.tensor_1d_compress._TN1D_COMPRESS_METHODS
quimb.tensor.tensor_1d_compress.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:

TensorNetwork

quimb.tensor.tensor_1d_compress.mps_gate_with_mpo_lazy(mps, mpo, inplace=False)[source]

Apply an MPO to an MPS lazily, i.e. nothing is contracted, but the new TN object has the same outer indices as the original MPS.

quimb.tensor.tensor_1d_compress.mps_gate_with_mpo_direct(mps, mpo, max_bond=None, cutoff=1e-10, inplace=False, **compress_opts)[source]

Apply an MPO to an MPS using the boundary compression method, that is, explicitly contracting site-wise to form a MPS-like TN, canonicalizing in one direction, then compressing in the other. This has the same scaling as the density matrix (dm) method, but a larger prefactor. It can still be faster for small bond dimensions however, and is potentially higher precision since it works in the space of singular values directly rather than singular values squared. It is not quite optimal in terms of error due to the compounding errors of the SVDs.

Parameters:

  • mps (MatrixProductState) – The MPS to gate.

  • mpo (MatrixProductOperator) – The MPO to gate with.

  • max_bond (int) – The maximum bond dimension to compress to.

  • cutoff (float, optional) – A dynamic threshold for discarding singular values when compressing.

  • compress_opts – Supplied to tensor_split().

quimb.tensor.tensor_1d_compress.mps_gate_with_mpo_dm(mps, mpo, max_bond=None, cutoff=1e-10, inplace=False, **compress_opts)[source]

Gate this MPS with an MPO, using the density matrix compression method.

Parameters:

  • mps (MatrixProductState) – The MPS to gate.

  • mpo (MatrixProductOperator) – The MPO to gate with.

  • max_bond (int, optional) – The maximum bond dimension to keep when compressing the double layer tensor network, if any.

  • cutoff (float, optional) – The truncation error to use when compressing the double layer tensor network, if any.

  • compress_opts – Supplied to tensor_split().

quimb.tensor.tensor_1d_compress.mps_gate_with_mpo_zipup(mps, mpo, max_bond=None, cutoff=1e-10, canonize=True, optimize='auto-hq', **compress_opts)[source]

Apply an MPO to an MPS using the ‘zip-up’ algorithm due to ‘Minimally Entangled Typical Thermal State Algorithms’, E.M. Stoudenmire & Steven R. White (https://arxiv.org/abs/1002.1305).

Parameters:

  • mps (MatrixProductState) – The MPS to gate.

  • mpo (MatrixProductOperator) – The MPO to gate with.

  • max_bond (int) – The maximum bond dimension to compress to.

  • cutoff (float, optional) – A dynamic threshold for discarding singular values when compressing.

  • 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 pseudo canonicalize the initial tensor network.

  • normalize (bool, optional) – Whether to normalize the final tensor network, making use of the fact that the output tensor network is in right canonical form.

  • 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.

  • compress_opts – Supplied to tensor_split().

Returns:

The compressed MPS, in right canonical form.

Return type:

MatrixProductState

quimb.tensor.tensor_1d_compress.mps_gate_with_mpo_zipup_first(mps, mpo, max_bond=None, max_bond_zipup=None, cutoff=1e-10, cutoff_zipup=None, canonize=True, optimize='auto-hq', **compress_opts)[source]

Apply an MPO to an MPS by first using the zip-up method with a larger bond dimension, then doing a regular compression sweep to the target final bond dimension. This avoids forming an intermediate MPS with bond dimension mps.max_bond() * mpo.max_bond().

Parameters:

  • mps (MatrixProductState) – The MPS to gate.

  • mpo (MatrixProductOperator) – The MPO to gate with.

  • max_bond (int) – The target final bond dimension.

  • max_bond_zipup (int, optional) – The maximum bond dimension to use when zip-up compressing the double layer tensor network. If not given, defaults to 2 * max_bond. Needs to be smaller than mpo.max_bond() for any savings.

  • cutoff (float, optional) – The truncation error to use when performing the final regular compression sweep.

  • cutoff_zipup (float, optional) – The truncation error to use when performing the zip-up compression.

  • canonize (bool, optional) – Whether to pseudo canonicalize the initial tensor network.

  • optimize (str, optional) – The contraction path optimizer to use.

  • compress_opts – Supplied to tensor_split() (both the zip-up and final sweep).

Returns:

The compressed MPS, in right canonical form.

Return type:

MatrixProductState

quimb.tensor.tensor_1d_compress.mps_gate_with_mpo_fit(mps, mpo, max_bond, **kwargs)[source]

Gate an MPS with an MPO using the variational fitting or DMRG-style method.

Parameters:
Returns:

The gated MPS.

Return type:

MatrixProductState

quimb.tensor.tensor_1d_compress.mps_gate_with_mpo_autofit(self, mpo, max_bond, cutoff=0.0, init_guess=None, **fit_opts)[source]

Fit a MPS to a MPO applied to an MPS using geometry generic versions of either ALS or autodiff. This is usually much less efficient that using the 1D specific methods.

Some nice alternatives to the default fit_opts:

  • method=”autodiff”

  • method=”als”, solver=”lstsq”

quimb.tensor.tensor_1d_compress.mps_gate_with_mpo_projector(self, mpo, max_bond, cutoff=1e-10, canonize=True, canonize_opts=None, inplace=False, **compress_opts)[source]

Apply an MPO to an MPS using local projectors, in the style of CTMRG or HOTRG, without using information beyond the neighboring 4 tensors.