def test_single_pm_graphs(self, parallel):
"""Tests that the number of photons is the same for modes i and n-i
in the special case of a graph with one single perfect matching
"""
n = 10 # size of the graph
approx_samples = 1000 # number of samples in the hafnian approximation
A = np.eye(n)[::-1]
n_mean = 2
nr_samples = 10
samples = hafnian_sample_graph(
A,
n_mean,
cutoff=5,
approx=True,
approx_samples=approx_samples,
samples=nr_samples,
parallel=parallel,
)
test_passed = True
for i in range(nr_samples):
s = samples[i]
for k in range(len(s) // 2):
if s[k] + s[-(k + 1)] % 2 == 1:
test_passed = False
assert test_passed
def test_probability_vacuum(self):
"""Tests that the probability of zero photons is correct"""
n = 4 # n is the size of the graph
approx_samples = 1000 # number of samples in the hafnian approximation
A = np.random.binomial(1, 0.5, (n, n))
A = np.triu(A)
A = A + np.transpose(A)
n_mean = 1.0
Q = gen_Qmat_from_graph(A, n_mean)
prob0 = np.abs(1 / (np.sqrt(np.linalg.det(Q))))
nr_samples = 100
samples = hafnian_sample_graph(A,
n_mean,
cutoff=5,
approx=True,
approx_samples=approx_samples,
samples=nr_samples)
nr_zeros = 0
for i in range(nr_samples):
photons = np.sum(samples[i])
if photons == 0:
nr_zeros += 1
prob0_estimate = nr_zeros / nr_samples
# allowed error in estimation
delta = 0.2
assert np.abs(prob0 - prob0_estimate) < delta
def test_hafnian_sample_graph(self):
"""Test hafnian sampling from a graph"""
A = np.array([[0, 3.0 + 4j], [3.0 + 4j, 0]])
n_samples = 1000
mean_n = 0.5
samples = hafnian_sample_graph(A, mean_n, samples=n_samples)
approx_mean_n = np.sum(samples) / n_samples
assert np.allclose(mean_n, approx_mean_n, rtol=2e-1)