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CHANGELOG.md

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Change Log

Unreleased

Fixed

  • Fix installation issue with tensorflow requirements on MACOS with M1 chip

  • Improve M1 macOS compayibility with unjit tensorflow ops

  • Fixed SVD backprop bug on jax backend of wide matrix

  • mps_input dtype auto correction enabled

0.1.1

Added

  • Add quoperator method to get QuOperator representation of the circuit unitary

  • Add coo_sparse_matrix_from_numpy method on backend, where the scipy coo matrix is converted to sparse tensor in corresponding backend

  • Add sparse tensor to scipy coo matrix implementation in numpy method

Changed

  • tc.quantum.PauliStringSum2COO, tc.quantum.PauliStringSum2Dense, and tc.quantum.heisenberg_hamiltonian now return the tensor in current backend format if numpy option sets to False. (Breaking change: previously, the return are fixed in TensorFlow format)

0.1.0

Added

  • DMCircuit also supports array instead of gate as the operator

Fixed

  • fix translation issue to qiskit when the input parameter is in numpy form

  • type conversion in measure API when high precision is set

  • fix bug in to_qiskit with new version qiskit

0.0.220509

Added

  • Add eigvalsh method on backend

Changed

  • post_select method return the measurement result int tensor now, consistent with cond_measure

  • Circuit.measure now point to measure_jit

0.0.220413

Added

  • Add expectation_ps method for DMCircuit

  • Add measure and sample for DMCircuit

Fixed

  • With Circuit.vis_tex, for the Circuit has customized input state, the default visualization is psi instead of all zeros now

  • general_kraus is synced with apply_general_kraus for DMCircuit

  • Fix dtype incompatible issue in kraus methods between status and prob

0.0.220402

Added

  • add utils.append to build function pipeline

  • add mean method on backends

  • add trigonometric methods on backends

  • add conditional_gate to support quantum ops based on previous measurment results

  • add expectation_ps as shortcut to get Pauli string expectation

  • add append and prepend to compose circuits

  • add matrix method to get the circuit unitary matrix

Changed

  • change the return information of unitary_kraus and general_kraus methods

  • add alias for any gate as unitary

0.0.220328

Added

  • add QuOperator convert tools which can convert MPO in the form of TensorNetwork and Quimb into MPO in the form of QuOperator

Changed

  • quantum Hamiltonian generation now support the direct return of numpy form matrix

Fixed

  • unitary_kraus and general_kraus API now supports the mix input of array and Node as kraus list

0.0.220318

Added

  • add gradient free scipy interface for optimization

  • add qiskit circuit to tensorcircuit circuit methods

  • add draw method on circuit from qiskit transform pipeline

Changed

  • futher refactor VQNHE code in applications

  • add alias sample for perfect_sampling method

  • optimize VQNHE pipeline for a more stable training loop (breaking changes in some APIs)

Fixed

  • Circuit inputs will convert to tensor first

0.0.220311

Added

  • add sigmoid method on backends

  • add MPO expectation template function for MPO evaluation on circuit

  • add operator_expectation in templates.measurements for a unified expectation interface

  • add templates.chems module for interface between tc and openfermion on quantum chemistry related tasks

  • add tc.Circuit to Qiskit QuantumCircuit transformation

Fixed

  • fix the bug in QuOperator.from_local_tensor where the dtype should always be in numpy context

  • fix MPO copy when apply MPO gate on the circuit

Changed

  • allow multi-qubit gate in multicontrol gate

0.0.220301

Added

  • new universal contraction analyse tools and pseudo contraction rehearsals for debug

  • add gather1d method on backends for 1d tensor indexing

  • add dataset module in template submodule for dataset preprocessing and embedding

  • MPO format quantum gate is natively support now

  • add multicontrol gates in MPO format

Fixed

  • fixed real operation on some methods in templates.measurements

Changed

  • add gatef key in circuit IR dict for the gate function, while replace gate key with the gate node or MPO (breaking change)

0.0.220126

Added

  • add td and sd gates for dagger version of T gate and S gate

  • add argmax and argmin as backend methods

  • add expectation_before methods for tc.Circuit for further manipulation on the tensornetwork

Changed

  • refined repr for tc.gates.Gate

  • expectation API now supports int index besides list indexes

Fixed

  • make consistent Gate return for channels

  • fixed bug on list optimizer for contraction

  • stability for QR operator in terms of automatic differentiation

0.0.220118

Added

  • add hessian method on backends

  • add further automatic pipelines for visualization by generating pdf or images

  • add reshape2 method on backends as a short cut to reshape a tensor with all legs 2-d

  • add reshapem method on backends to reshape any tensor as a square matrix

  • add controlled and ocontrolled API to generate more gates

  • add crx, cry, crz gate on Circuit

  • add __repr__ and __str__ for backend object

  • tc.expectation now support ket arg as quvector form

Fixed

  • sizen correctly returns 1 for tensor of no shape

  • fixed convert_to_tensor bug in numpy backend in TensorNetwork

  • any_gate also support Gate format instead of matrix

  • matrix_for_gate works now for backends more than numpy

Changed

  • expectation API now also accepts plain tensor instead of tc.Gate.

  • DMCircuit and DMCircuit2 are all pointing the efficent implementations (breaking changes)

0.0.220106

Added

  • add solve method on backends to solve linear equations

  • add full quantum natural gradient examples and qng method in experimental module

  • add concat method to backends

  • add stop_gradient method to backends

  • add has_aux arg on vvag method

  • add imag method on backends

  • add Circuit.vis_tex interface that returns the quantikz circuit latex

Changed

  • contractor, dtype and backend set are default to return objects, with tc.runtime_backend("jax") as K or K = tc.set_backend("jax") could work

  • change perfect_sampling to use measure_jit behind the scene

  • anygate automatically reshape the unitary input to 2-d leg for users' good

  • quantum.renyi_entropy computation with correct prefactor

  • Circuit gate can provided other names by name attr

  • example_block support param auto reshape for users' good

Fixed

  • make four algorithms for quantum natural gradient consistent and correct

  • torch real is now a real

0.0.211223

Added

  • add quantum.heisenberg_hamiltonian for hamiltonian generation shortcut

  • add has_aux parameter in backend methods grad and value_and_grad, the semantic syntax is the same as jax

  • add optimizer class on tensorflow and jax backend, so that a minimal and unified backend agnostic optimizer interface is provided

  • add quantum.mutual_information, add support on mixed state for quantum.reduced_density_matrix

  • add jvp methods for tensorflow, jax, torch backends, and ensure pytree support in jvp and vjp interfaces for tensorflow and jax backends; also ensure complex support for jvp and vjp

  • add jacfwd and jacrev for backend methods (experimental API, may have bugs and subject to changes)

Fixed

  • fix matmul bug on tensornetwork tensorflow backend

Changed

  • delete qcode IR for Circuit, use qir instead (breaking changes)

  • basic circuit running is ok on pytorch backend with some complex support fixing

0.0.211216

Added

  • add get_random_state and random_split methods to backends

  • add qir representation of circuit, c.to_qir() and Circuit.from_qir() methods

  • fine-grained control on backend, dtype and contractor setup: tc.set_function_backend() for function level decorator and tc.runtime_backend() as with context manager

  • add state_centric decorator in tc.templates.blocks to transform circuit-to-circuit funtion to state-to-state function

  • add interfaces.scipy_optimize_interface to transform quantum function into scipy.optimize.minimize campatible form

Fixed

  • avoid error on watch non tf.Tensor in tensorflow backend grad method

  • circuit preprocessing simplification with only single qubit gates

  • avoid the bug when random from jax backend with jitted function

  • refresh the state cache in Circuit when new gate is applied

Changed

  • refactor tc.gates (breaking API on rgate -> r_gate, iswapgate -> iswap_gate)