def test_error_probability_from_array():
H = csr_matrix(np.array([[1,1,0,0,0],[0,1,1,0,0],
[0,0,1,1,0],[0,0,0,1,1]]))
m = Matching(H, error_probabilities=np.array([0.,0.,0.,0.,1.]))
assert np.array_equal(m.add_noise()[0], np.array([0,0,0,0,1]))
assert np.array_equal(m.add_noise()[1], np.array([0,0,0,1,1]))
m = Matching(H, error_probabilities=np.array([0.,0.,0.,0.,0.]))
assert np.array_equal(m.add_noise()[0], np.array([0,0,0,0,0]))
assert np.array_equal(m.add_noise()[1], np.array([0,0,0,0,0]))
m = Matching(H, error_probabilities=0.0)
assert np.array_equal(m.add_noise()[0], np.array([0,0,0,0,0]))
assert np.array_equal(m.add_noise()[1], np.array([0,0,0,0,0]))
m = Matching(H, error_probabilities=1.0)
assert np.array_equal(m.add_noise()[0], np.array([1,1,1,1,1]))
assert np.array_equal(m.add_noise()[1], np.array([0,0,0,0,0]))
def test_add_noise_with_boundary():
g = nx.Graph()
for i in range(11):
g.add_edge(i, i + 1, qubit_id=i, error_probability=(i + 1) % 2)
for i in range(5, 12):
g.nodes()[i]['is_boundary'] = True
m = Matching(g)
noise, syndrome = m.add_noise()
assert sum(syndrome) == 6
assert np.array_equal(noise, (np.arange(11) + 1) % 2)
assert m.boundary == list(range(5, 12))
assert np.array_equal(syndrome,
np.array([1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]))
def test_add_noise():
p = 0.1
N = 1000
std = (p * (1 - p) / N)**0.5
g = nx.Graph()
for i in range(N):
g.add_edge(i,
i + 1,
qubit_id=i,
weight=-np.log(p),
error_probability=p)
m = Matching(g)
for i in range(5):
noise, syndrome = m.add_noise()
assert (sum(syndrome) % 2) == 0
assert (p - 5 * std) * N < sum(noise) < (p + 5 * std) * N
for i in range(1, N - 1):
assert syndrome[i] == (noise[i - 1] + noise[i]) % 2
def test_add_noise_to_unweighted_returns_none():
m = Matching(csr_matrix(np.array([[1,1,0],[0,1,1]])))
assert m.add_noise() == None
m = Matching(csr_matrix(np.array([[1,1,0],[0,1,1]])),
error_probabilities=np.array([0.5,0.7,-0.1]))
assert m.add_noise() == None
def test_add_noise_without_error_probabilities_returns_none():
m = Matching(csr_matrix(np.array([[1, 1, 0], [0, 1, 1]])))
assert m.add_noise() is None
m = Matching(csr_matrix(np.array([[1, 1, 0], [0, 1, 1]])),
error_probabilities=np.array([0.5, 0.7, -0.1]))
assert m.add_noise() is None
