def test_bad_syndrome_raises_value_error():
g = nx.Graph()
g.add_edge(0, 1, qubit_id=0)
g.add_edge(1, 2, qubit_id=1)
m = Matching(g)
with pytest.raises(ValueError):
noise = m.decode('test')
def test_boundaries_from_networkx():
g = nx.Graph()
g.add_edge(0,1, qubit_id=0)
g.add_edge(1,2, qubit_id=1)
g.add_edge(2,3, qubit_id=2)
g.add_edge(3,4, qubit_id=3)
g.add_edge(4,5, qubit_id=4)
g.add_edge(0,5, qubit_id=-1, weight=0.0)
g.nodes()[0]['is_boundary'] = True
g.nodes()[5]['is_boundary'] = True
m = Matching(g)
assert m.boundary == [0,5]
assert np.array_equal(m.decode(np.array([0,1,0,0,0,0])), np.array([1,0,0,0,0]))
assert np.array_equal(m.decode(np.array([0,0,1,0,0])), np.array([1,1,0,0,0]))
assert np.array_equal(m.decode(np.array([0,0,1,1,0])), np.array([0,0,1,0,0]))
assert np.array_equal(m.decode(np.array([0,0,0,1,0])), np.array([0,0,0,1,1]))
def test_local_matching_connected(cluster_size):
g = nx.Graph()
qid = 0
for i in range(cluster_size):
g.add_edge(i, i + 1, weight=1.0, qubit_id=qid)
qid += 1
g.add_edge(cluster_size,
cluster_size + 1,
weight=2 * cluster_size,
qubit_id=qid)
qid += 1
for i in range(cluster_size + 1, 2 * cluster_size + 1):
g.add_edge(i, i + 1, weight=1.0, qubit_id=qid)
qid += 1
m = Matching(g)
m.decode([1] * (cluster_size + 1) * 2, num_neighbours=cluster_size)
def test_decode_all_neighbours():
g = nx.Graph()
g.add_edge(0, 1, qubit_id=0)
g.add_edge(1, 2, qubit_id=1)
m = Matching(g)
noise = m.decode([1,0,1], num_neighbours=None)
assert np.array_equal(noise, np.array([1,1]))
def test_unweighted_stabiliser_graph_from_networkx():
w = nx.Graph()
w.add_edge(0, 1, qubit_id=0, weight=7.0)
w.add_edge(0, 5, qubit_id=1, weight=14.0)
w.add_edge(0, 2, qubit_id=2, weight=9.0)
w.add_edge(1, 2, qubit_id=-1, weight=10.0)
w.add_edge(1, 3, qubit_id=3, weight=15.0)
w.add_edge(2, 5, qubit_id=4, weight=2.0)
w.add_edge(2, 3, qubit_id=-1, weight=11.0)
w.add_edge(3, 4, qubit_id=5, weight=6.0)
w.add_edge(4, 5, qubit_id=6, weight=9.0)
m = Matching(w)
assert(m.num_qubits == 7)
assert(m.num_stabilisers == 6)
assert(m.stabiliser_graph.shortest_path(3, 5) == [3, 2, 5])
assert(m.stabiliser_graph.distance(5, 0) == pytest.approx(11.0))
assert(np.array_equal(
m.decode(np.array([1,0,1,0,0,0])),
np.array([0,0,1,0,0,0,0]))
)
with pytest.raises(ValueError):
m.decode(np.array([1,1,0]))
with pytest.raises(ValueError):
m.decode(np.array([1,1,1,0,0,0]))
assert(np.array_equal(
m.decode(np.array([1,0,0,0,0,1])),
np.array([0,0,1,0,1,0,0]))
)
assert(np.array_equal(
m.decode(np.array([0,1,0,0,0,1])),
np.array([0,0,0,0,1,0,0]))
)
def test_isolated_negative_weight(nn):
m = Matching()
m.add_edge(0, 1, 0, 1)
m.add_edge(1, 2, 1, -10)
m.add_edge(2, 3, 2, 1)
m.add_edge(3, 0, 3, 1)
c, w = m.decode([0, 1, 1, 0], return_weight=True, num_neighbours=nn)
assert np.array_equal(c, np.array([0, 1, 0, 0]))
assert w == -10
def test_double_weight_matching():
w = nx.Graph()
w.add_edge(0, 1, qubit_id=0, weight=0.97)
w.add_edge(2, 3, qubit_id=1, weight=1.98)
w.add_edge(0, 2, qubit_id=2, weight=1.1)
w.add_edge(1, 3, qubit_id=3, weight=1.2)
m = Matching(w)
assert (list(m.decode(np.array([1, 1, 1,
1]))) == list(np.array([0, 0, 1, 1])))
def test_mwpm_noisy_decode(n, z_err, c_expected):
fn = "css_2D-toric_(4,4)_[[18,2,3]]_Hx.npz"
H = load_npz(os.path.join(TEST_DIR, 'data', fn))
m = Matching(H, repetitions=z_err.shape[1])
n_all = np.cumsum(n, 0) % 2
z_noiseless = H.dot(n_all.T) % 2
z_noisy = (z_noiseless + z_err) % 2
z_noisy[:,1:] = (z_noisy[:,1:] - z_noisy[:,:-1]) % 2
c = m.decode(z_noisy)
assert(np.array_equal(c, c_expected))
def test_mwpm_decode_method():
fn = "css_2D-toric_(4,4)_[[18,2,3]]_Hx.npz"
H = load_npz(os.path.join(TEST_DIR, 'data', fn))
m = Matching(H)
n = np.zeros(H.shape[1], dtype=int)
n[5] = 1
n[10] = 1
z = H.dot(n) % 2
c = m.decode(z)
assert(np.array_equal(c,n))
def test_negative_and_positive_in_matching(nn):
g = nx.Graph()
g.add_edge(0, 1, fault_ids=0, weight=1)
g.add_edge(1, 2, fault_ids=1, weight=-10)
g.add_edge(2, 3, fault_ids=2, weight=1)
g.add_edge(3, 0, fault_ids=3, weight=1)
m = Matching(g)
c, w = m.decode([0, 1, 0, 1], return_weight=True, num_neighbours=nn)
assert np.array_equal(c, np.array([0, 1, 1, 0]))
assert w == -9
def test_matching_correct():
g = nx.Graph()
g.add_edge(0, 1, weight=1.24, qubit_id=0)
g.add_edge(1, 2, weight=1.31, qubit_id=1)
g.add_edge(2, 3, weight=1.41, qubit_id=2)
g.add_edge(0, 4, weight=1.51, qubit_id=3)
g.add_edge(1, 5, weight=1.65, qubit_id=4)
g.add_edge(2, 6, weight=1.15, qubit_id=5)
g.add_edge(3, 7, weight=1.44, qubit_id=6)
g.add_edge(4, 5, weight=1.70, qubit_id=7)
g.add_edge(5, 6, weight=1.9, qubit_id=8)
g.add_edge(6, 7, weight=1.12, qubit_id=9)
g.add_edge(4, 8, weight=1.87, qubit_id=10)
g.add_edge(5, 9, weight=1.91, qubit_id=11)
g.add_edge(6, 10, weight=1.09, qubit_id=12)
g.add_edge(7, 11, weight=1.21, qubit_id=13)
g.add_edge(8, 9, weight=1.99, qubit_id=14)
g.add_edge(9, 10, weight=1.01, qubit_id=15)
g.add_edge(10, 11, weight=1.06, qubit_id=16)
g.add_edge(8, 12, weight=1.16, qubit_id=17)
g.add_edge(9, 13, weight=1.38, qubit_id=18)
g.add_edge(10, 14, weight=1.66, qubit_id=19)
g.add_edge(11, 15, weight=1.58, qubit_id=20)
g.add_edge(12, 13, weight=1.12, qubit_id=21)
g.add_edge(13, 14, weight=1.50, qubit_id=22)
g.add_edge(14, 15, weight=1.00, qubit_id=23)
m = Matching(g)
assert sum(m.decode([0] * 16, num_neighbours=20)) == 0
assert sum(m.decode([0] * 16, num_neighbours=None)) == 0
z = np.zeros(16, dtype=np.uint8)
z[0] = 1
z[5] = 1
z[6] = 1
z[11] = 1
z[14] = 1
z[15] = 1
assert np.array_equal(
m.decode(z, num_neighbours=20).nonzero()[0],
np.array([0, 4, 12, 16, 23]))
assert np.array_equal(
m.decode(z, num_neighbours=None).nonzero()[0],
np.array([0, 4, 12, 16, 23]))
def test_negative_weight_repetition_code(nn):
m = Matching()
m.add_edge(0, 1, 0, -1)
m.add_edge(1, 2, 1, -1)
m.add_edge(2, 3, 2, -1)
m.add_edge(3, 4, 3, -1)
m.add_edge(4, 5, 4, -1)
m.add_edge(5, 0, 5, -1)
c, w = m.decode([0, 1, 1, 0, 0, 0], return_weight=True, num_neighbours=nn)
assert np.array_equal(c, np.array([1, 0, 1, 1, 1, 1]))
assert w == -5
def test_matching_weight(n, num_neighbours):
p = 0.4
H = repetition_code(n)
noise = np.random.rand(n) < p
weights = np.random.rand(n)
s = H @ noise % 2
m = Matching(H, spacelike_weights=weights)
corr, weight = m.decode(s,
num_neighbours=num_neighbours,
return_weight=True)
expected_weight = np.sum(weights[corr == 1])
assert expected_weight == pytest.approx(weight)
def test_odd_3d_syndrome_raises_value_error():
H = csr_matrix(np.array([[1,1,0],[0,1,1]]))
m = Matching(H)
with pytest.raises(ValueError):
m.decode(np.array([[1,0],[0,0]]))
def test_3d_syndrome_raises_value_error_when_repetitions_not_set():
H = csr_matrix(np.array([[1, 1, 0], [0, 1, 1]]))
m = Matching(H)
with pytest.raises(ValueError):
m.decode(np.array([[1, 0], [0, 0]]))