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CHANGELOG.md

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Change Log

Unreleased

0.6.0

Added

  • Add native support for rxx, ryy and rzz gates for translation from qiskit

  • Add from_openqasm and from_openqasm_file methods for Circuit

  • Add circuit_params argument for translation from qiskit to make the interface universal and consistent with other from_ methods

  • Add shifts tuple parameter for experimental.parameter_shift_grad API so that we can also customize finite difference gradient from this method

  • Add std method for backends

  • Add NoiseModel class to programmably configure the global error model when simulating the quantum circuit

  • Add tc.channels.composedkraus to compose the different Kraus operators as a new one

  • Add direct support for noise model via sample_expectation_ps and expectation methods, both Monte Carlo trajectory and density matrix evolution approaches are supported

Changed

  • Improve the efficiency of sample_expectation_ps method by using cached state

Fixed

  • Fixed unitary_kraus of Circuit class support for multi-qubit kraus channels, previous implementation fails to reshape the multi-qubit kraus tensor as matrix

  • Fixed kraus_to_super_gate bug when multi-qubit kraus channels are presented on tensorflow backend

0.5.0

Added

  • Finished quantum noise modeling and simulation development stage 1. Add more quantum channels and the differentiable transformation between different channel forms. Add readout error support for sample and sample_expectation_ps methods.

  • Add new parameter shift gradient API that supports finite measurement shots and the corresponding example scripts

  • Add openqasm format transformation method c.to_openqasm()

  • Add native support for phase and cphase gates when transforming to qiskit

  • Add native support for rxx, ryy, rzz and u, cu gates when transforming to qiskit

  • Add native support for u gate when transforming from qiskit

  • Add circuit from_qsim_file method to load Google random circuit structure

  • Add searchsorted method for backend

  • Add probability_sample method for backend as an alternative for random_choice since it supports status as external randomness format

  • Add status support for sample and sample_expection_ps methods

Changed

  • The inner mechanism for sample_expectation_ps is changed to sample representation from count representation for a fast speed

Fixed

  • Fixed the breaking change introduced in jax 0.3.18, jax._src is no longer imported into the from the public jax namespace.

  • tc.quantum.correlation_from_samples now fix the sign error with odd number of spins

  • Updated to the latest version of mypy and get rid of lots of type: ignored

  • Fix the dtype bug when float is pass to u gate or phase gate

  • Fix to qiskit bug when parameterized gate has default nonset parameters

  • Fix iswap gate translation to qiskit with support for parameters

0.4.1

Added

  • Add support for weighted graph QAOA in tc.templates.blocks.QAOA_block

  • Add AbstractCircuit to and from json capability (experimental support, subject to change)

  • Add alias for sd and td gate

  • Add phase gate and cphase gate for the circuit

  • Add U gate and CU gate following OpenQASM 3.0 convention

  • Add tc.gates.get_u_parameter to solve the three Euler angle for the U gate given the matrix

  • Add GateVF.ided() method to kron quantum gate with identity gate

  • Add batched_parameters_structures.py example to demonstrate nested vmap and architecture search possibility

  • Add gate_count method on AbstractCircuit to count gates of given type in the circuit

  • Add gate_summary method on AbstractCircuit to count gate by type as a dict

  • Add ccx as another alias for toffoli gate

Changed

  • Seperate channel auto register for circuit class with unitary and general case

  • The old standalone depolarizing implementation can now be called via c.depolarizing_reference

Fixed

  • Move iswap gate to vgates list

  • Fix possible bug when vmap is nested in different order (only affect tensorflow backend)

  • Fix bug when multi input function accept the same variable in different args and gradient or jvp/vjp is required (only affect tensorflow backend)

  • Fix the use of rev over rev jacobian for hessian method and back to the efficient solution of fwd over rev order due to the solution of nested vmap issue on tf backend

  • Identify potential bug in unitary_kraus2 implementation, change to unitary_kraus instead

0.4.0

Added

  • Add sample_expectation_ps method for BaseCircuit, which measure the Pauli string expectation considering measurement shots

  • Add alias expps for expectation_ps and sexpps for sampled_expectation_ps

  • Add counts_d2s and counts_s2d in quantum module to transform different representation of measurement shots results

  • Add vmap enhanced parameter_shift_grad in experimental module (API subjects to change)

  • Add parameter_shift.py script in examples showcasing how to use parameter shift grad wrapper

  • Add vmap_randomness.py script in examples showcasing how to vmap external random generators

  • Add noise_sampling_jit.py script showcasing how real device simulation with sample method is efficiently implemented with Monte Carlo and jit

  • Add jit support and external random management for tc.quantum.measurement_counts

  • Add MPO gate support for multiple qubit gates in MPSCircuit simulator

  • Add the expectation_ps method to MPSCircuit (moving to AbstractCircuit)

  • Add six format of measurement results support and their transformation in quantum module

  • Add format option in Circuit.sample while maintain the backward compatibility

  • Add tc.utils.arg_alias which is a decorator that adds alias argument for function with the doc fixed accordingly

  • Add quantum channel auto resgisteration method in Circuit class

Changed

  • rxx, ryy, rzz gates now has 1/2 factor before theta consitent with rx, ry, rz gates. (breaking change)

  • replace status arguments in sample method as random_generator (new convention: status for 0, 1 uniform randomness and random_generator for random key) (breaking change)

  • Rewrite the expectation method of MPSCircuit to make it general

  • Adjusted the initialization method for MPSCircuit (move the from_wavefunction method and allow QuVector input) (breaking change)

  • tc.quantum.measurement_counts aliased as tc.quantum.measurement_results and change the function arguments (breaking change)

  • Refactor backend to use multiple inheritance approach instead of reflection method

Fixed

  • Add jit support for sample method when allow_state=True

  • Fix the bug that 128 type is converted to 64 value

  • Fix arg_alias bug when the keyword arguments is None by design

  • Fix arg_alias when the docstring for each argument is in multiple lines

  • Noise channel apply methods in DMCircuit can also absorb status keyword (directly omitting it) for a consistent API with Circuit

0.3.1

Added

  • Add overload of plus sign for TensorFlow SparseTensor

  • Add support of dict or list of tensors as the input for tc.KerasLayer

  • Add support of multiple tensor inputs for tc.TorchLayer

  • Both gate index and expectation(_ps) now support negative qubit index, eg. c.H(-1) with the same meaning as numpy indexing

  • Add tc.templates.measurements.parameterized_local_measurements for local Pauli string evaluation

Changed

  • Change pytest xdist option in check_all.sh to -n auto

  • Further refactor the circuit abstraction as AbstractCircuit and BaseCircuit

Fixed

  • Add sgates, vgates, mpogates and gate_aliases back to BaseCircuit for backward compatibility

  • Add auto conversion of dtype for unitary gate

0.3.0

Added

  • Add device and device_move methods on backend with universal device string representation

  • Add to_qir, from_qir, to_circuit, inverse, from_qiskit, to_qiskit, sample, prepend, append, cond_measurment, select_gate method for DMCircuit class

  • Add status arguments as external randomness for perfect_sampling and measure methods

  • DMCircuit now supports mps_inputs and mpo_dminputs

  • Add decorator tc.interfaces.args_to_tensor to auto convert function inputs as tensor format

Changed

  • Refactor circuit and dmcircuit with common methods now in basecircuit.py, and merge dmcircuit.py and dmcircuit2.py, now DMCircuit supports MPO gate and qir representation

Fixed

  • Patially solve the issue with visualization on cond_measure (#50)

0.2.2

Added

  • PyTorch backend support multi pytrees version of tree_map

  • Add dtype backend method which returns the dtype string

  • Add TensorFlow interface

  • Add to_dlpack and from_dlpack method on backends

  • Add enable_dlpack option on interfaces and torchnn

  • Add inverse method for Circuit (#26)

Changed

  • Refactor interfaces code as a submodule and add pytree support for args

  • Change the way to register global setup internally, so that we can skip the list of all submodules

  • Refactor the tensortrans code to a pytree perspective

Fixed

  • Fixed numpy method bug in pytorch backend when the input tensor requires grad (#24) and when the tensor is on GPU (#25)

  • Fixed TorchLayer parameter list auto registeration

  • Pytorch interface is now device aware (#25)

0.2.1

Added

  • Add enable_lightcone option in circuit expectation method, where only gates within casual lightcone of local observable is contracted.

  • Add benchmark function into utils

Fixed

  • Fixed a vital bug on circuit expectation evaluation, a wrongly transposed operator connection is fixed.

  • Name passed in gate application now works as Node name

0.2.0

Added

  • Add PyTorch nn Module wrapper in torchnn

  • Add reverse, mod, left_shift, right_shift, arange methods on backend

  • Brand new sample API with batch support and sampling from state support

  • add more methods in global namespace, and add alias KerasLayer/TorchLayer

Fixed

  • Fixed bug in merge single gates when all gates are single-qubit ones

Changed

  • The default contractor enable preprocessing feature where single-qubit gates are merged firstly

0.1.3

Added

  • Add more type auto conversion for tc.gates.Gate as inputs

  • Add tree_flatten and tree_unflatten method on backends

  • Add torch optimizer to the backend agnostic optimizer abstraction

Changed

  • Refactor the tree utils, add native torch support for pytree utils

Fixed

  • grad in torch backend now support pytrees

  • fix float parameter issue in translation to qiskit circuit (#19)

0.1.2

Added

  • Add rxx, ryy and rzz gate

Fixed

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

  • Improve M1 macOS compatibility 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)