Changelog

Release notes for quimb.

v1.8.5 (unreleased)

Enhancements:

• expose qtn.edge_coloring as top level function and allow layers to be returned grouped.

• add docstring for tn.contract_compressed

v1.8.4 (2024-07-20)

Bug fixes:

• fix for MPS sampling with fixed seed (GH 247 and PR 248)

• fix for mps_gate_with_mpo_lazy (GH 246).

v1.8.3 (2024-07-10)

Enhancements:

• support for numpy v2.0 and scipy v1.14

• add MPS sampling: MatrixProductState.sample_configuration and MatrixProductState.sample (generating multiple samples) and use these for CircuitMPS.sample and CircuitPermMPS.sample.

• add basic .plot() method for SimpleUpdate classes

• add edges_1d_chain for generating 1D chain edges

• operatorbuilder: better coefficient placement for long range MPO building

v1.8.2 (2024-06-12)

Enhancements:

• TNOptimizer can now accept an arbitrary pytree (nested combination of dicts, lists, tuples, etc. with TensorNetwork, Tensor or raw array_like objects as the leaves) as the target object to optimize.

• TNOptimizer can now directly optimize Circuit objects, returning a new optimized circuit with updated parameters.

• Circuit: add .copy(), .get_params() and .set_params() interface methods.

• Update generic TN optimizer docs.

• add tn.gen_inds_loops for generating all loops of indices in a TN.

• add tn.gen_inds_connected for generating all connected sets of indices in a TN.

• make SVD fallback error catching more generic (PR #238)

• fix some windows + numba CI issues.

• approx_spectral_function add plotting and tracking

• add dispatching to various tensor primitives to allow overriding

v1.8.1 (2024-05-06)

Enhancements:

• CircuitMPS now supports multi qubit gates, including arbitrary multi-controls (which are treated in a low-rank manner), and faster simulation via better orthogonality center tracking.

• add CircuitPermMPS

• add MatrixProductState.gate_nonlocal for applying a gate, supplied as a raw matrix, to a non-local and arbitrary number of sites. The kwarg contract="nonlocal" can be used to force this method, or the new option "auto-mps" will select this method if the gate is non-local (GH 230)

• add MatrixProductState.gate_with_mpo for applying an MPO to an MPS, and immediately compressing back to MPS form using tensor_network_1d_compress

• add MatrixProductState.gate_with_submpo for applying an MPO acting only of a subset of sites to an MPS

• add MatrixProductOperator.from_dense for constructing MPOs from dense matrices, including an only subset of sites

• add MatrixProductOperator.fill_empty_sites for ‘completing’ an MPO which only has tensors on a subset of sites with (by default) identities

• MatrixProductState and MatrixProductOperator, now support the sites kwarg in common constructors, enabling the TN to act on a subset of the full L sites.

• add TensorNetwork.drape_bond_between for ‘draping’ an existing bond between two tensors through a third

• add Tensor.new_ind_pair_with_identity

• TN2D, TN3D and arbitrary geom classical partition function builders (TN_classical_partition_function_from_edges) now all support outputs= kwarg specifying non-marginalized variables

• add simple dense 1-norm belief propagation algorithm D1BP

• add qtn.enforce_1d_like for checking whether a tensor network is 1D-like, including automatically adding strings of identities between non-local bonds, expanding applicability of tensor_network_1d_compress

• add MatrixProductState.canonicalize as (by default non-inplace) version of canonize, to follow the pattern of other tensor network methods. canonize is now an alias for canonicalize_ [note trailing underscore].

• add MatrixProductState.left_canonicalize as (by default non-inplace) version of left_canonize, to follow the pattern of other tensor network methods. left_canonize is now an alias for left_canonicalize_ [note trailing underscore].

• add MatrixProductState.right_canonicalize as (by default non-inplace) version of right_canonize, to follow the pattern of other tensor network methods. right_canonize is now an alias for right_canonicalize_ [note trailing underscore].

Bug fixes:

• Circuit.apply_gate_raw: fix kwarg bug (PR 226)

• fix for retrieving opt_einsum.PathInfo for single scalar contraction (GH 231)

v1.8.0 (2024-04-10)

Breaking Changes

• all singular value renormalization is turned off by default

• TensorNetwork.compress_all now defaults to using some local gauging

Enhancements:

• add quimb.tensor.tensor_1d_compress.py with functions for compressing generic 1D tensor networks (with arbitrary local structure) using various methods. The methods are:

  • The ‘direct’ method: tensor_network_1d_compress_direct

  • The ‘dm’ (density matrix) method: tensor_network_1d_compress_dm

  • The ‘zipup’ method: tensor_network_1d_compress_zipup

  • The ‘zipup-first’ method: tensor_network_1d_compress_zipup_first

  • The 1 and 2 site ‘fit’ or sweeping method: tensor_network_1d_compress_fit

  • … and some more niche methods for debugging and testing.

  And can be accessed via the unified function tensor_network_1d_compress. Boundary contraction in 2D can now utilize any of these methods.

• add quimb.tensor.tensor_arbgeom_compress.py with functions for compressing arbitrary geometry tensor networks using various methods. The methods are:

  • The ‘local-early’ method: tensor_network_ag_compress_local_early

  • The ‘local-late’ method: tensor_network_ag_compress_local_late

  • The ‘projector’ method: tensor_network_ag_compress_projector

  • The ‘superorthogonal’ method: tensor_network_ag_compress_superorthogonal

  • The ‘l2bp’ method: tensor_network_ag_compress_l2bp

  And can be accessed via the unified function tensor_network_ag_compress. 1D compression can also fall back to these methods.

• support PBC in tn2d.contract_hotrg, tn2d.contract_ctmrg, tn3d.contract_hotrg and the new function tn3d.contract_ctmrg.

• support PBC in gen_2d_bonds and gen_3d_bonds, with cyclic kwarg.

• support PBC in TN2D_rand_hidden_loop and TN3D_rand_hidden_loop, with cyclic kwarg.

• support PBC in the various base PEPS and PEPO construction methods.

• add tensor_network_apply_op_op for applying ‘operator’ TNs to ‘operator’ TNs.

• tweak tensor_network_apply_op_vec for applying ‘operator’ TNs to ‘vector’ or ‘state’ TNs.

• add tnvec.gate_with_op_lazy method for applying ‘operator’ TNs to ‘vector’ or ‘state’ TNs like \(x \rightarrow A x\).

• add tnop.gate_upper_with_op_lazy method for applying ‘operator’ TNs to the upper indices of ‘operator’ TNs like \(B \rightarrow A B\).

• add tnop.gate_lower_with_op_lazy method for applying ‘operator’ TNs to the lower indices of ‘operator’ TNs like \(B \rightarrow B A\).

• add tnop.gate_sandwich_with_op_lazy method for applying ‘operator’ TNs to the upper and lower indices of ‘operator’ TNs like \(B \rightarrow A B A^\dagger\).

• unify all TN summing routines into `tensor_network_ag_sum, which allows summing any two tensor networks with matching site tags and outer indices, replacing specific MPS, MPO, PEPS, PEPO, etc. summing routines.

• add rand_symmetric_array, rand_tensor_symmetric TN2D_rand_symmetric for generating random symmetric arrays, tensors and 2D tensor networks.

Bug fixes:

• fix scipy sparse monkey patch for scipy>=1.13 (GH 222)

• fix autoblock bug where connected sectors were not being merged (GH 223)

v1.7.3 (2024-02-08)

Enhancements:

• qu.randn: support dist="rademacher".

• support dist and other randn options in various TN builders.

Bug fixes:

• restore fallback (to scipy.linalg.svd with driver=’gesvd’) behavior for truncated SVD with numpy backend.

v1.7.2 (2024-01-30)

Enhancements:

• add normalized=True option to tensor_network_distance for computing the normalized distance between tensor networks: \(2 |A - B| / (|A| + |B|)\), which is useful for convergence checks. Tensor.distance_normalized and TensorNetwork.distance_normalized added as aliases.

• add TensorNetwork.cut_bond for cutting a bond index

Bug fixes:

• removed import of deprecated numba.generated_jit decorator.

v1.7.1 (2024-01-30)

Enhancements:

• add TensorNetwork.visualize_tensors for visualizing the actual data entries of an entire tensor network.

• add ham.build_mpo_propagator_trotterized for building a trotterized propagator from a local 1D hamiltonian. This also includes updates for creating ‘empty’ tensor networks using TensorNetwork.new, and building up gates from empty tensor networks using TensorNetwork.gate_inds_with_tn.

• add more options to Tensor.expand_ind and Tensor.new_ind: repeat tiling mode and random padding mode.

• tensor decomposition: make eigh_truncated backend agnostic.

• tensor_compress_bond: add reduced="left" and reduced="right" modes for when the pair of tensors is already in a canonical form.

• add qtn.TN2D_embedded_classical_ising_partition_function for constructing 2D (triangular) tensor networks representing all-to-all classical ising partition functions.

Bug fixes:

• fix bug in kruas_op when operator spanned multiple subsystems (GH 214)

• fix bug in qr_stabilized when the diagonal of R has significant imaginary parts.

• fix bug in quantum discord computation when the state was diagonal (GH 217)

v1.7.0 (2023-12-08)

Breaking Changes

• Circuit : remove target_size in preparation for all contraction specifications to be encapsulated at the contract level (e.g. with cotengra)

• some TN drawing options (mainly arrow options) have changed due to the backend change detailed below.

Enhancements:

• TensorNetwork.draw: use quimb.schematic for main backend="matplotlib" drawing. Enabling:

  1. multi tag coloring for single tensors

  2. arrows and labels on multi-edges

  3. better sizing of tensors using absolute units

  4. neater single tensor drawing, in 2D and 3D

• add quimb.schematic.Drawing from experimental submodule, add example docs at schematic - manual drawing. Add methods text_between, wedge, line_offset and other tweaks for future use by main TN drawing.

• upgrade all contraction to use cotengra as the backend

• Circuit : allow any gate to be controlled by any number of qubits.

• Circuit : support for parsing openqasm2 specifications now with custom and nested gate definitions etc.

• add is_cyclic_x, is_cyclic_y and is_cyclic_z to TensorNetwork2D and TensorNetwork3D.

• add TensorNetwork.compress_all_1d for compressing generic tensor networks that you promise have a 1D topology, without casting as a TensorNetwork1D.

• add MatrixProductState.from_fill_fn for constructing MPS from a function that fills the tensors.

• add Tensor.idxmin and Tensor.idxmax for finding the index of the minimum/maximum element.

• 2D and 3D classical partition function TN builders: allow output indices.

• quimb.experimental.belief_propagation: add various 1-norm/2-norm dense/lazy BP algorithms.

Bug fixes:

• fixed bug where an output index could be removed by squeezing when performing tensor network simplifications.

v1.6.0 (2023-09-10)

Breaking Changes

• Quantum circuit RZZ definition corrected (angle changed by -1/2 to match qiskit).

Enhancements:

• add OpenQASM 2.0 parsing support: Circuit.from_openqasm2_file,

• Circuit: add RXX, RYY, CRX, CRY, CRZ, toffoli, fredkin, givens gates

• truncate TN pretty html reprentation to 100 tensors for performance

• add Tensor.sum_reduce and Tensor.vector_reduce

• contract_compressed, default to ‘virtual-tree’ gauge

• add TN_rand_tree

• experimental.operatorbuilder: fix parallel and heisenberg builder

• make parametrized gate generation even more robost (ensure matching types so e.g. tensorflow can be used)

Bug fixes:

• fix gauge size check for some backends

v1.5.1 (2023-07-28)

Enhancements:

• add MPS_COPY().

• add ‘density matrix’ and ‘zip-up’ MPO-MPS algorithms.

• add drop_tags option to tensor_contract()

• compress_all_simple(), allow cutoff.

• add structure checking debug methods: Tensor.check() and TensorNetwork.check().

• add several direction contraction utility functions: get_symbol(), inds_to_eq() and array_contract().

Bug fixes:

• Circuit: use stack for more robust parametrized gate generation

• fix for gate_with_auto_swap() for i > j.

• fix bug where calling tn.norm() would mangle indices.

v1.5.0 (2023-05-03)

Enhancements

• refactor ‘isometrize’ methods including new “cayley”, “householder” and “torch_householder” methods. See quimb.tensor.decomp.isometrize().

• add compute_reduced_factor() and insert_compressor_between_regions() methos, for some RG style algorithms.

• add the mode="projector" option for 2D tensor network contractions

• add HOTRG style coarse graining and contraction in 2D and 3D. See coarse_grain_hotrg(), contract_hotrg(), coarse_grain_hotrg(), and contract_hotrg(),

• add CTMRG style contraction for 2D tensor networks: contract_ctmrg()

• add 2D tensor network ‘corner double line’ (CDL) builders: TN2D_corner_double_line()

• update the docs to use the furo theme, myst_nb for notebooks, and several other sphinx extensions.

• add the 'adabelief' optimizer to TNOptimizer as well as a quick plotter: plot()

• add initial 3D plotting methods for tensors networks ( TensorNetwork.draw(dim=3, backend='matplotlib3d') or TensorNetwork.draw(dim=3, backend='plotly') ). The new backend='plotly' can also be used for 2D interactive plots.

• Update HTN_from_cnf() to handle more weighted model counting formats.

• Add cnf_file_parse()

• Add random_ksat_instance()

• Add TN_from_strings()

• Add convert_to_2d()

• Add TN2D_rand_hidden_loop()

• Add convert_to_3d()

• Add TN3D_corner_double_line()

• Add TN3D_rand_hidden_loop()

• various optimizations for minimizing computational graph size and construction time.

• add 'lu', 'polar_left' and 'polar_right' methods to tensor_split().

• add experimental arbitrary hamilotonian MPO building

• TensorNetwork: allow empty constructor (i.e. no tensors representing simply the scalar 1)

• drop_tags(): allow all tags to be dropped

• tweaks to compressed contraction and gauging

• add jax, flax and optax example

• add 3D and interactive plotting of tensors networks with via plotly.

• add pygraphiviz layout options

• add combine() for unified handling of combining tensor networks potentially with structure

• add HTML colored pretty printing of tensor networks for notebooks

• add quimb.experimental.cluster_update.py

Bug fixes:

• fix qr_stabilized() bug for strictly upper triangular R factors.

v1.4.2 (2022-11-28)

Enhancements

• move from versioneer to to setuptools_scm for versioning

v1.4.1 (2022-11-28)

Enhancements

• unify much functionality from 1D, 2D and 3D into general arbitrary geometry class quimb.tensor.tensor_arbgeom.TensorNetworkGen

• refactor contraction, allowing using cotengra directly

• add visualize() for visualizing the actual data entries of an arbitrarily high dimensional tensor

• add Gate class for more robust tracking and manipulation of gates in quantum Circuit simulation

• tweak TN drawing style and layout

• tweak default gauging options of compressed contraction

• add compute_hierarchical_grouping()

• add as_network()

• add inds_size()

• add get_hyperinds()

• add outer_size()

• improve group_inds()

• refactor tensor decompositiona and ‘isometrization’ methods

• begin supporting pytree specifications in TNOptimizer, e.g. for constants

• add experimental submodule for new sharing features

• register tensor and tensor network objects with jax pytree interface (PR 150)

• update CI infrastructure

Bug fixes:

• fix force atlas 2 and weight_attr bug (GH 126)

• allow unpickling of PTensor objects (GH 128, PR 131)

v1.4.0 (2022-06-14)

Enhancements

• Add 2D tensor network support and algorithms

• Add 3D tensor network infrastructure

• Add arbitrary geometry quantum state infrastructure

• Many changes to TNOptimizer

• Many changes to TN drawing

• Many changes to Circuit simulation

• Many improvements to TN simplification

• Make all tag and index operations deterministic

• Add tensor_network_sum(), tensor_network_distance() and fit()

• Various memory and performance improvements

• Various graph generators and TN builders

v1.3.0 (2020-02-18)

Enhancements

• Added time dependent evolutions to Evolution when integrating a pure state - see Time-Dependent Evolutions - as well as supporting LinearOperator defined hamiltonians (PR 40).

• Allow the Evolution callback compute= to optionally access the Hamiltonian (PR 49).

• Added quimb.tensor.tensor_core.Tensor.randomize() and quimb.tensor.tensor_core.TensorNetwork.randomize() to randomize tensor and tensor network entries.

• Automatically squeeze tensor networks when rank-simplifying.

• Add compress_site() for compressing around single sites of MPS etc.

• Add MPS_ghz_state() and MPS_w_state() for building bond dimension 2 open boundary MPS reprentations of those states.

• Various changes in conjunction with autoray to improve the agnostic-ness of tensor network operations with respect to the backend array type.

• Add new_bond() on top of quimb.tensor.tensor_core.Tensor.new_ind() and quimb.tensor.tensor_core.Tensor.expand_ind() for more graph orientated construction of tensor networks, see Graph Orientated Tensor Network Creation.

• Add the fsim() gate.

• Make the parallel number generation functions use new numpy 1.17+ functionality rather than randomgen (which can still be used as the underlying bit generator) (PR 50)

• TN: rename contraction_complexity to contraction_width().

• TN: update quimb.tensor.tensor_core.TensorNetwork.rank_simplify(), to handle hyper-edges.

• TN: add quimb.tensor.tensor_core.TensorNetwork.diagonal_reduce(), to automatically collapse all diagonal tensor axes in a tensor network, introducing hyper edges.

• TN: add quimb.tensor.tensor_core.TensorNetwork.antidiag_gauge(), to automatically flip all anti-diagonal tensor axes in a tensor network allowing subsequent diagonal reduction.

• TN: add quimb.tensor.tensor_core.TensorNetwork.column_reduce(), to automatically identify tensor axes with a single non-zero column, allowing the corresponding index to be cut.

• TN: add quimb.tensor.tensor_core.TensorNetwork.full_simplify(), to iteratively perform all the above simplifications in a specfied order until nothing is left to be done.

• TN: add num_tensors and num_indices attributes, show num_indices in __repr__.

• TN: various improvements to the pytorch optimizer (PR 34)

• TN: add some built-in 1D quantum circuit ansatzes: circ_ansatz_1D_zigzag(), circ_ansatz_1D_brickwork(), and circ_ansatz_1D_rand().

• TN: add parametrized tensors PTensor and so trainable, TN based quantum circuits – see Tensor Network Training of Quantum Circuits.

Bug fixes:

• Fix consistency of fidelity() by making the unsquared version the default for the case when either state is pure, and always return a real number.

• Fix a bug in the 2D system example for when j != 1.0

• Add environment variable QUIMB_NUMBA_PAR to set whether numba should use automatic parallelization - mainly to fix travis segfaults.

• Make cache import and initilization of petsc4py and slepc4py more robust.

v1.2.0 (2019-06-06)

Enhancements

• Added kraus_op() for general, noisy quantum operations

• Added projector() for constructing projectors from observables

• Added measure() for measuring and collapsing quantum states

• Added cprint() pretty printing states in computational basis

• Added simulate_counts() for simulating computational basis counts

• TN: Add quimb.tensor.tensor_core.TensorNetwork.rank_simplify()

• TN: Add quimb.tensor.tensor_core.TensorNetwork.isel()

• TN: Add quimb.tensor.tensor_core.TensorNetwork.cut_iter()

• TN: Add 'split-gate' gate mode

• TN: Add TNOptimizer for tensorflow based optimization of arbitrary, contstrained tensor networks.

• TN: Add quimb.tensor.tensor_1d.Dense1D.rand()

• TN: Add connect() to conveniently set a shared index for tensors

• TN: make many more tensor operations agnostic of the array backend (e.g. numpy, cupy, tensorflow, …)

• TN: allow align_TN_1D() to take an MPO as the first argument

• TN: add build_sparse()

• TN: add quimb.tensor.tensor_core.Tensor.unitize() and quimb.tensor.tensor_core.TensorNetwork.unitize() to impose unitary/isometric constraints on tensors specfied using the left_inds kwarg

• Many updates to tensor network quantum circuit (quimb.tensor.circuit.Circuit) simulation including:

  • quimb.tensor.circuit.CircuitMPS

  • quimb.tensor.circuit.CircuitDense

  • 49-qubit depth 30 circuit simulation example Quantum Circuits

• Add from quimb.gates import * as shortcut to import X, Z, CNOT, ....

• Add U_gate() for parametrized arbitrary single qubit unitary

Bug fixes:

• Fix pkron for case len(dims) == len(inds) (GH 17, PR 18).

• Fix qarray printing for older numpy versions

• Fix TN quantum circuit bug where Z and X rotations were swapped

• Fix variable bond MPO building (GH 22) and L=2 DMRG

• Fix norm(X, 'trace') for non-hermitian matrices

• Add autoray as dependency (GH 21)