def test_seed():
"""Tests that seed method does reset the random number generators"""
n_samples = 10
mean_n = 1.0
r = np.arcsinh(np.sqrt(mean_n))
V = np.array([[np.exp(2 * r), 0.0], [0.0, np.exp(-2 * r)]])
seed(137)
first_sample = hafnian_sample_state(V, n_samples)
second_sample = hafnian_sample_state(V, n_samples)
seed(137)
first_sample_p = hafnian_sample_state(V, n_samples)
second_sample_p = hafnian_sample_state(V, n_samples)
assert np.array_equal(first_sample, first_sample_p)
assert np.array_equal(second_sample, second_sample_p)
hafnian_sample_state,
hafnian_sample_graph,
torontonian_sample_state,
torontonian_sample_graph,
hafnian_sample_classical_state,
torontonian_sample_classical_state,
seed,
photon_number_sampler,
generate_hafnian_sample,
generate_torontonian_sample,
hafnian_sample_graph_rank_one,
)
from thewalrus.quantum import gen_Qmat_from_graph, density_matrix_element, probabilities
from thewalrus.symplectic import two_mode_squeezing
seed(137)
rel_tol = 3.0
abs_tol = 1.0e-10
def TMS_cov(r, phi, hbar=2):
"""returns the covariance matrix of a TMS state"""
S = two_mode_squeezing(r, phi)
return S @ S.T * hbar / 2
class TestHafnianSampling:
"""Tests for hafnian sampling"""