thewalrus 0.16.0 on Python PyPI

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 can now 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

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

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 renormalize probabilities. #250

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.

This release contains contributions from (in alphabetical order):

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

thewalrus 0.16.0 Release 0.16.0 on Python PyPI