thewalrus 0.13.0 on Python PyPI

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

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

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

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

This release contains contributions from (in alphabetical order):

Theodor Isacsson, Josh Izaac, Filippo Miatto, Nicolas Quesada

thewalrus 0.13.0 Version 0.13.0 on Python PyPI