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Release 0.21.0-dev

New features

Breaking changes

Improvements

Bug fixes

Documentation

Contributors

This release contains contributions from (in alphabetical order):

Release 0.20.0

New features

  • Implementation of gaussian boson sampling and gaussian boson sampling with threshold detectors. (#338)

  • New function to produce Bloch-Messiah decomposition of symplectic matrices. (#352)

Breaking changes

Improvements

  • Added function to extend single mode symplectic to act on multiple modes. (#347)
  • Added function to compute grouped (total) click probabilities for GBS setups using threshold detectors. The function uses the positive P-distribution simulation method of Drummond et al.. (#348)

Bug fixes

  • Remove redundant call of Qmat, Amat from generate_hafnian_sample. (#343)

Documentation

Contributors

This release contains contributions from (in alphabetical order):

Mikhail Andrenkov, Sebastián Duque, Jacob Hastrup, Antonín Hoskovec, Martin Houde, Benjamin Lanthier, Dominic Leclerc, Filippo Miatto, Will McCutcheon, Brandon Turcotte, Jiaqi Zhao

Version 0.19.0

New features

  • New functions for calculating properties of distinguishable squeezed states of light having passed through an interferometer. #326

  • New function ltor is added which allows threshold_detector_prob to act more consistently on displaced and zero-mean Gaussian states. #317

  • New functions for threshold detection probabilities of Fock states, the Bristolian (brs) and the Unitary Bristolian (ubrs). #316

  • Entanglement measures entanglement_entropy and log_negativity for bipartite Gaussian states are added to the quantum submodule. #332

  • New functions, recursive_hafnian and solve added in the _hafnian module. #325

  • New function to check if a matrix is symplectic is_symplectic. #334.

  • Adds support for Python 3.10. #337

Improvements

Bug fixes

  • Permanent algorithms handle 0x0 cases correctly. #320

Contributors

This release contains contributions from (in alphabetical order):

Jake Bulmer, Luke Helt, Martin Houde, Theodor Isacsson, Benjamin Lanthier, Fabian Laudenbach, Dominic Leclerc, Gregory Morse, Nicolas Quesada, Brandon Turcotte, Jiaqi Zhao

Version 0.18.0

New features

  • Python module for the La Budde method of computing characteristic polynomials. #304

Improvements

  • Permanent algorithms are implemented in Python using Numba just-in-time compilation. #300

  • Hafnian algorithms are implemented in Python using Numba just-in-time compilation. #311

  • Documentation is updated to include the characteristic polynomials and decompositions modules. #312

Bug fixes

  • Makes modules reachable via the global namespace, instead of requiring importing the modules explicitly. #312

    import thewalrus as tw
tw.samples.generate_torontonian_sample

Breaking Changes

  • The Walrus is no longer dependent on C++, and all C++-related code and documentation is removed. Instead, all code has been ported to Python using just-in-time compilation to improve performance. #311

Contributors

This release contains contributions from (in alphabetical order):

Theodor Isacsson, Benjamin Lanthier, Dominic Leclerc, Nicolas Quesada, Brandon Turcotte, Trevor Vincent, Jiaqi Zhao

Version 0.17.0

Improvements

  • Python installation no longer requires repoze.lru. #293

  • Multidimensional Hermite polynomials are now implemented in Numba, hence reducing the C++ dependencies of The Walrus. #295

  • Updates missing figures in the "Basics of Hafnians and Loop Hafnians" documentation. #288

Contributors

This release contains contributions from (in alphabetical order):

Mikhail Andrenkov, Sebastián Duque

Version 0.16.2

Bug fixes

  • hermite_multidimensional_numba can now handle a cutoff of type np.ndarray with shape=[]. #283

Contributors

This release contains contributions from (in alphabetical order):

Filippo Miatto

Version 0.16.1

Improvements

  • Faster implementation of hermite_multidimensional_numba and hermite_multidimensional_numba_grad. #280

Bug fixes

  • Updates the samples.generate_torontonian_sample function to ensure probabilities are normalized. #250

  • Pins Numba to version <0.54 to avoid binary incompatibilities with the 1.21 release of NumPy. #250

Contributors

This release contains contributions from (in alphabetical order):

Josh Izaac, Filippo Miatto, Nicolas Quesada.

Version 0.16.0

New features

  • Adds the function hafnian_sparse to compute sparse loop hafnians (pure Python implementation). #245

  • The symplectic.squeezing function is now generalized to multiple modes of single mode squeezing. #249

  • Adds a function symplectic.passive_transformation which allows for Gaussian states to be transformed by arbitrary non-unitary, non-square linear optical transformations. #249

  • The torontonian_sample_state function now can sample displaced Gaussian states. #248

  • Adds the function hafnian_banded to calculate the hafnian of a banded matrix. #246

  • Adds the functions hermite_multidimensional_numba and grad_hermite_multidimensional_numba to calculate renormalized multidimensional Hermite polynomials and its gradients using numba. #251

  • Adds the functions mzgate and grad_mzgate to calculate the Fock representation of the Mach-Zehnder gate and its gradients. #257

  • Adds the ability to calculate n-body photon number distributions using the function n_body_marginals. #253

  • Adds the ability to calculate cumulants and arbitrary expectation values of products of powers of photon numbers with the functions photon_number_cumulant and photon_number_moment respectively. #264

  • Adds support for calculating the permanent using the BBFG algorithm and changes this to the default method for calculating permanents. #267

  • Adds the ability to calculate click cumulants in threshold detection with the function click_cumulant. #264

Improvements

  • Speeds up the calculation of photon number variances/covariances. #244

  • Updates documentation for the the tor function. #265

  • Numba methods for multidimensional hermite can now detect dtype automatically. #271

Bug fixes

  • Corrects bug in the function photon_number_covar that gave incorrect results when the covariance between two modes with finite displacements was calculated. #264

  • Fixes a bug in setup.py that would cause the build to fail when using miniforge for M1 macs. #273

  • Updates the samples.generate_hafnian_sample function to renormalizing probabilities. #250

Breaking changes

  • Torontonians and approximations to the hafnian for non-negative matrices are no longer calculated in C++ using the Eigen software library. Instead, they are now calculated in pure Python using Numba. These changes have the nice result of making The Walrus compilable from source using only a C++ compiler. #262 #259.

Contributors

This release contains contributions from (in alphabetical order):

Ali Asadi, Jake Bulmer, Timjan Kalajdzievski, Filippo Miatto, Nicolas Quesada, Yuan Yao

Version 0.15.1

Bug fixes

  • Builds The Walrus binaries against an older version of NumPy, to avoid a breaking ABI change in NumPy 1.20. #240

Contributors

This release contains contributions from (in alphabetical order):

Josh Izaac

Version 0.15.0

New features

  • Adds the function random_banded_interferometer to generate unitary matrices with a given bandwidth. #208

  • Adds the function tvd_cutoff_bounds to calculate bounds in the total variation distance between a Fock-truncated and an ideal GBS distribution. #210

  • Adds function for calculating threshold detection probabilities for Gaussian states with displacement. #220

  • Adds new functions total_photon_number_distribution and characteristic_function to study properties of the total photon number distribution of a k identical lossy squeezers. #230

  • Adds new functions xxpp_to_xpxp and xpxp_to_xxpp in the symplectic module to swap the ordering of the quadrature operators in vectors and matrices. #237

Improvements

  • The hafnians and loop hafnians of diagonal matrices are now calculated in polynomial time. #212

  • Refactors setup.py to avoid issues with CFLAGS. #229

  • The fidelity function in quantum/gaussian_checks.py is rewritten to add clarity. #226

  • Simplifies logic of normal_ordered_expectation by removing mutually cancelling np.conj. #228

Bug fixes

  • Removes unnecessary np.real_if_close statements in quantum/fock_tensors.py causing the probabilities to not be normalized. #215

  • Fixes the prefactor in pure_state_amplitude. #231

Contributors

This release contains contributions from (in alphabetical order):

Jack Brown, Jake Bulmer, Rachel Chadwick, Stefano Paesani, Nicolas Quesada

Version 0.14.0

New features

  • Adds the function find_classical_subsystem that tries to find a subset of the modes with a classical covariance matrix. #193

  • Adds the functions mean_number_of_clicks and variance_number_of_clicks that calculate the first and second statistical moments of the total number of clicks in a Gaussian state centered at the origin. #195

  • Adds the module decompositions with the function williamson to find the Williamson decomposition of an even-size positive-semidefinite matrix. #200

  • Adds the loop_hafnian_quad function to the Python interface for converting double into quad, do the calculations in quad and then return a double. #201

Improvements

  • Introduces a new faster and significantly more accurate algorithm to calculate power traces allowing to speed up the calculation of loop hafnians #199

  • The quantum module has been refactored and organized into sub-modules. Several functions have been renamed, while the old names are being deprecated. #197

  • Adds support for C++14 #202

  • pytest-randomly is added to the test suite to improve testing and avoid stochastically failing tests. #205

  • Modifies the function input_validation to use np.allclose for checking the symmetry of the input matrices. #206

  • Modifies the function _hafnian to calculate efficiently loop hafnians of diagonal matrices. #206

Breaking changes

  • Removes the redundant function normal_ordered_complex_cov. #194

  • Renames the function mean_number_of_clicks to be mean_number_of_click_graph. #195

Contributors

This release contains contributions from (in alphabetical order):

Theodor Isacsson, Nicolas Quesada, Trevor Vincent

Version 0.13.0

New features

  • Adds a new algorithm for hafnians of matrices with low rank. #166

  • Adds a function to calculate the fidelity between two Gaussian quantum states. #169

  • Adds a new module, thewalrus.random, to generate random unitary, symplectic and covariance matrices. #169

  • Adds new functions normal_ordered_expectation, photon_number_expectation and photon_number_squared_expectation in thewalrus.quantum to calculate expectation values of products of normal ordered expressions and number operators and their squares. #175

  • Adds the function hafnian_sample_graph_rank_one in thewalrus.samples to sample from rank-one adjacency matrices. #174

Improvements

  • Adds parallelization support using Dask for quantum.probabilities. #161

  • Removes support for Python 3.5. #163

  • Changes in the interface and speed ups in the functions in the thewalrus.fock_gradients module. #164

  • Improves documentation of the multidimensional Hermite polynomials. #166

  • Improves speed of fock_tensor when the symplectic matrix passed is also orthogonal. #166

Bug fixes

  • Fixes Numba decorated functions not rendering properly in the documentation. #173

  • Solves the issue with quantum and samples not being rendered in the documentation or the TOC. #173

  • Fix bug where quantum and samples were not showing up in the documentation. #182

Breaking changes

  • The functions in thewalrus.fock_gradients are now separated into functions for the gradients and the gates. Moreover, they are renamed, for instance Dgate becomes displacement and its gradient is now grad_displacement. #164

Contributors

This release contains contributions from (in alphabetical order):

Theodor Isacsson, Josh Izaac, Filippo Miatto, Nicolas Quesada

Version 0.12.0

New features

  • Adds the ability to calculate the mean number of photons in a given mode of a Gaussian state. #148

  • Adds the ability to calculate the photon number distribution of a pure or mixed state using generate_probabilities. #152

  • Allows to update the photon number distribution when undergoing loss by using update_probabilities_with_loss. #152

  • Allows to update the photon number distribution when undergoing noise update_probabilities_with_noise. #153

  • Adds a brute force sampler photon_number_sampler that given a (multi-)mode photon number distribution generates photon number samples. #152

  • Adds the ability to perform the Autonne-Takagi decomposition of a complex-symmetric matrix using autonne from the symplectic module. #154

Improvements

  • Improves the efficiency of Hermite polynomial calculation in hermite_multidimensional.hpp. #141

  • Implements parallelization with Dask for sampling from the Hafnian/Torontonian of a Gaussian state. #145

Bug fixes

  • Corrects the issue with hbar taking a default value when calling state_vector, pure_state_amplitude, and density_matrix_element #149

Contributors

This release contains contributions from (in alphabetical order):

Theodor Isacsson, Nicolas Quesada, Kieran Wilkinson

Version 0.11.0

New features

  • Introduces the renormalized hermite polynomials. These new polynomials improve the speed and accuracy of thewalrus.quantum.state_vector and thewalrus.quantum.density_matrix and also hafnian_batched and hermite_multimensional when called with the optional argument renorm=True. #108

  • Adds functions for calculating the covariance for the photon number distribution of a Gaussian state including a function for the full covariance matrix. #137

  • Adds support for Python 3.8. #138

Improvements

  • Updates the reference that should be used when citing The Walrus. #102

  • Updates and improves the speed and accuracy of thewalrus.quantum.fock_tensor. #107

  • Add OpenMP support to the repeated moment hafnian code. #120

  • Improves speed of the functions in hermite_multidimensional.hpp. #123

  • Improves speed of the functions in thewalrus.fock_gradients by doing calls to optimized functions in hermite_multidimensional.hpp. #123

  • Further improves speed of the functions thewalrus.fock_gradients by writing explicit recursion relations for a given number of modes. #129

  • Adds the functions find_scaling_adjacency_matrix_torontonian and mean_number_of_clicks that allow to fix the mean number of clicks when doing threshold detection sampling and allow to calculate the mean of clicks generated by a scaled adjacency matrix. #136

Bug fixes

  • Corrects typos in the random number generation in the C++ unit tests. #118

  • Corrects typos in describing the repeated-moment algorithm of Kan in the documentation. #104

  • Removes paper.{md,pdf,bib} from the repository now that The Walrus paper is published in Journal of Open Source Software #106

  • Updates the S2gate to use the correct definition. #130

  • Corrects the issue with hbar taking a default value when calculating mu in the density matrix function [#134] (XanaduAI#134)

Contributors

This release contains contributions from (in alphabetical order):

Theodor Isacsson, Josh Izaac, Filippo Miatto, Nicolas Quesada, Trevor Vincent, Kieran Wilkinson

Version 0.10.0

New features

  • Adds the function thewalrus.quantum.fock_tensor that returns the Fock space tensor corresponding to a Symplectic transformation in phase space. #90

  • Adds the thewalrus.fock_gradients module which provides the Fock representation of a set of continuous-variable universal gates in the Fock representation and their gradients. #96

Improvements

  • Unifies return values of all symplectic gates in the thewalrus.symplectic module. #81

  • Removes unnecessary citations in the tutorials. #92

  • Improves the efficiency of the multidimensional Hermite polynomials implementation and simplifies a number of derived functions. #93

Bug fixes

  • Fixes a bug in the calculation of the state vector in thewalrus.quantum.state_vector. This bug was found and fixed while implementing thewalrus.quantum.fock_tensor. #90

Contributors

This release contains contributions from (in alphabetical order):

Josh Izaac, Nicolas Quesada

Version 0.9.0

New features

  • Adds a symplectic module symplectic which allows easy access to symplectic transformations and covariance matrices of Gaussian states. #78

Improvements

  • Adds a quick reference section in the documentation. #75

Bug fixes

  • Solves issue #70 related to the index ordering in thewalrus.quantum.density_matrix.

Contributors

This release contains contributions from (in alphabetical order):

Josh Izaac, Nicolas Quesada

Version 0.8.0

New features

  • Adds classical sampling of permanents of positive definite matrices in csamples. #61

  • The Walrus gallery with examples of nongaussian state preparations that can be studied using the functions from the quantum module. #55

Improvements

  • Updates the bibliography of the documentation with recently published articles. #51

Bug fixes

  • Important bugfix in quantum. This bug was detected by running the tests in test_integration. #48

  • Corrects the Makefile so that it uses the environment variable pointing to Eigen (if available). #58

  • Removes any reference to Fortran in the Makefile. #58

Contributors

This release contains contributions from (in alphabetical order):

Luke Helt, Josh Izaac, Soran Jahangiri, Nicolas Quesada, Guillaume Thekkadath

Version 0.7.0

New features

  • Hafnian library has been renamed to The Walrus, and the low-level libhafnian C++ library renamed to libwalrus. #34

  • Added a seed function thewalrus.samples.seed, in order to make the sampling algorithms deterministic for testing purposes. #29

  • Added support for the batched hafnian; hafnian_batched (in Python) and libwalrus::hermite_multidimensional_cpp (in C++). This is a newly added algorithm that allows a batch of photon number statistics to be computed for a given quantum Gaussian state. #21

  • Adds the ability to sample from Gaussian states with finite means. #25

Improvements

  • Permanent Fortran code was ported to C++, with improvements including support for quadruple precision and summation using the fsum algorithm. #20

  • Reorganization of the repository structure; C++ source code folder has been renamed from src to include, C++ tests have been moved into their own top-level directory tests, and the hafnian/lib subpackage has been removed in favour of a top-level thewalrus/libwalrus.so module. #34

  • Added additional references to the bibliography #30

  • Adds documentation related to permanents, sampling states with nonzero displacement, sampling of classical states, and multidimensional Hermite polynomials and batched hafnians. #27

  • Simplifies the hafnian sampling functions #25

  • Test improvements, including replacing custom tolerance checks with np.allclose(). #23

Bug fixes

  • Minor typos corrected in the documentation. #33 #28 #34

Contributors

This release contains contributions from (in alphabetical order):

Brajesh Gupt, Josh Izaac, Nicolas Quesada

Version 0.6.1

New features

  • Added two new sampling functions in hafnian.sample, allowing efficient sampling from classical Gaussian states:

    • hafnian_sample_classical_state
    • torontonian_sample_classical_state

  • Added functions to calculate the probability amplitudes directly for pure states:

    • pure_state_amplitude
    • state_vector

  • Added utility functions to check the validity of covariance matrices:

    • is_valid_cov
    • is_pure_cov
    • is_classical_cov

Improvements

  • All functions in the quantum package now take as arguments xp-covariance matrices and vectors of means for consistency.

Contributors

This release contains contributions from (in alphabetical order):

Nicolas Quesada

Version 0.6.0

New features

  • Added a new sampling submodule hafnian.sample, allowing sampling from the underlying hafnian/Torontonian distribution of graphs/Gaussian states

  • Documentation overhaul: now contains some of the best and most up-to-date information about hafnians, loop hafnians, and Torontonians

  • C++ library has been significantly improved, tested, and refactored

Improvements

  • Ported the hafnian_approx.F90 Fortran file to C++

  • The Torontonian function is now parallelized via OpenMP

  • Tests and the C++ header library have been refactored

  • Addition of new C++ tests using Googletest

  • C++ library is now documented via Doxygen; this is integrated directly into Sphinx

Contributors

This release contains contributions from (in alphabetical order):

Brajesh Gupt, Josh Izaac, Nicolas Quesada