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 precision matrices into quad precision, doing the calculations in quad precision, and then return the result as 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 0.13.0rc1

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

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, 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

Contributors

This release contains contributions from (in alphabetical order):

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

Version 0.10.1

Small release to add a DOI on Zenodo.

Version 0.10

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

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

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