Classical sampling algorithms

Module name: thewalrus.csamples

This submodule provides access to classical sampling algorithms for thermal states going through an interferometer specified by a real orthogonal matrix. The quantum state to be sampled is specified by a positive semidefinite real matrix and a mean photon number. The algorithm implemented here was first derived in

For more precise details of the implementation see

  • Soran Jahangiri, Juan Miguel Arrazola, Nicolás Quesada, and Nathan Killoran. “Point processes with Gaussian boson sampling” Phys. Rev. E 101, 022134, (2020)..

Summary

rescale_adjacency_matrix_thermal(A, n_mean)

Returns the scaling parameter by which the adjacency matrix A should be rescaled so that the Gaussian state that encodes it has a total mean photon number n_mean for thermal sampling.

rescale_adjacency_matrix(A, n_mean, scale[, …])

Returns the scaling parameter by which the adjacency matrix A should be rescaled so that the Gaussian state that encodes it has a total mean photon number n_mean.

generate_thermal_samples(ls, O[, num_samples])

Generates samples of the Gaussian state in terms of the mean photon number parameter ls and the interferometer O.

Code details

generate_thermal_samples(ls, O, num_samples=1)[source]

Generates samples of the Gaussian state in terms of the mean photon number parameter ls and the interferometer O.

Parameters
  • ls (array) – squashing parameters

  • O (array) – Orthogonal matrix representing the interferometer

  • num_samples – Number of samples to generate

Returns

samples

Return type

list(array

rescale_adjacency_matrix(A, n_mean, scale, check_positivity=True, check_symmetry=True, rtol=1e-05, atol=1e-08)[source]

Returns the scaling parameter by which the adjacency matrix A should be rescaled so that the Gaussian state that encodes it has a total mean photon number n_mean.

Parameters
  • A (array) – Adjacency matrix, assumed to be positive semi-definite and real

  • n_mean (float) – Mean photon number of the Gaussian state

  • scale (float) – Determines whether to rescale the matrix for thermal sampling (scale = 1.0) or for squashed sampling (scale = 2.0)

  • check_positivity (bool) – Checks if the matrix A is positive semidefinite

  • check_symmetry (bool) – Checks if the matrix is symmetric

  • rtol – relative tolerance for the checks

  • atol – absolute tolerance for the checks

Returns

rescaled eigenvalues and eigenvectors of the matrix A

Return type

tuple(array,array)

rescale_adjacency_matrix_thermal(A, n_mean, check_positivity=True, check_symmetry=True, rtol=1e-05, atol=1e-08)[source]

Returns the scaling parameter by which the adjacency matrix A should be rescaled so that the Gaussian state that encodes it has a total mean photon number n_mean for thermal sampling.

Parameters
  • A (array) – Adjacency matrix, assumed to be positive semi-definite and real

  • n_mean (float) – Mean photon number of the Gaussian state

  • check_positivity (bool) – Checks if the matrix A is positive semidefinite

  • check_symmetry (bool) – Checks if the matrix is symmetric

  • rtol – relative tolerance for the checks

  • atol – absolute tolerance for the checks

Returns

rescaled eigenvalues and eigenvectors of the matrix A

Return type

tuple(array,array)