def test_measurement_error_probabilities_and_probability_raises_value_error():
H = np.array([[1, 1, 0], [0, 1, 1]])
with pytest.raises(ValueError):
m = Matching()
m.load_from_check_matrix(H, spacelike_weights=np.array([0.3, 0.7, 0.9]),
measurement_error_probabilities=[0.1, 0.1], repetitions=3,
measurement_error_probability=[0.1, 0.1])
def test_local_matching_clusters(cluster_size):
g = nx.Graph()
qid = 0
for i in range(cluster_size):
g.add_edge(i, i + 1, weight=1.0, fault_ids=qid)
qid += 1
g.add_edge(cluster_size,
cluster_size + 1,
weight=3 * cluster_size,
fault_ids=qid)
qid += 1
for i in range(cluster_size + 1, 2 * cluster_size + 1):
g.add_edge(i, i + 1, weight=1.0, fault_ids=qid)
qid += 1
m = Matching(g)
m.decode(np.ones((cluster_size + 1) * 2), num_neighbours=cluster_size)
for i in range(1, cluster_size + 1):
with pytest.raises(BlossomFailureException):
local_matching(m.matching_graph,
np.arange(m.num_detectors),
num_neighbours=i,
max_attempts=1)
for i in range(cluster_size + 1, 2 * cluster_size):
local_matching(m.matching_graph,
np.arange(m.num_detectors),
num_neighbours=i,
max_attempts=1)
local_matching(m.matching_graph,
np.arange(m.num_detectors),
num_neighbours=1,
max_attempts=int(np.log2(cluster_size + 1)) + 2)
def test_mwpm_from_networkx():
g = nx.Graph()
g.add_edge(0, 1, fault_ids=0)
g.add_edge(0, 2, fault_ids=1)
g.add_edge(1, 2, fault_ids=2)
m = Matching(g)
assert(isinstance(m.matching_graph, MatchingGraph))
assert(m.num_detectors == 3)
assert(m.num_fault_ids == 3)
assert(m.matching_graph.distance(0,2) == 1)
assert(m.matching_graph.shortest_path(0,2) == [0,2])
g = nx.Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
m = Matching(g)
assert(isinstance(m.matching_graph, MatchingGraph))
assert(m.num_detectors == 3)
assert(m.num_fault_ids == 0)
assert(m.matching_graph.distance(0,2) == 1)
assert(m.matching_graph.shortest_path(0,2) == [0,2])
g = nx.Graph()
g.add_edge(0, 1, weight=1.5)
g.add_edge(0, 2, weight=1.7)
g.add_edge(1, 2, weight=1.2)
m = Matching(g)
assert(isinstance(m.matching_graph, MatchingGraph))
assert(m.num_detectors == 3)
assert(m.num_fault_ids == 0)
assert(m.matching_graph.distance(0,2) == pytest.approx(1.7))
assert(m.matching_graph.shortest_path(0,2) == [0,2])
def test_timelike_weights(t_weights, expected_edges):
H = np.array([[1, 1, 0], [0, 1, 1]])
m = Matching()
m.load_from_check_matrix(H, spacelike_weights=np.array([0.3, 0.7, 0.9]),
timelike_weights=t_weights, repetitions=3)
es = set((tuple(sorted([u, v])), d["weight"]) for u, v, d in m.edges())
assert es == expected_edges
def test_weighted_mwpm_from_array():
H = csc_matrix([[1,0],[1,1],[0,1]])
m = Matching(H, spacelike_weights=np.array([1., 2.]))
assert m.matching_graph.distance(0, 1) == 1.
assert m.matching_graph.distance(1, 2) == 2.
with pytest.raises(ValueError):
m = Matching(H, spacelike_weights=np.array([1.]))
def test_bad_syndrome_raises_value_error():
g = nx.Graph()
g.add_edge(0, 1, fault_ids=0)
g.add_edge(1, 2, fault_ids=1)
m = Matching(g)
with pytest.raises(TypeError):
m.decode('test')
def test_decode_with_multiple_components():
g = nx.Graph()
g.add_edge(0, 1)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(3, 4)
g.add_edge(4, 5)
g.add_edge(3, 5)
m = Matching(g)
for z in (np.array([0]), np.arange(6)):
with pytest.raises(ValueError):
local_matching(m.matching_graph, z)
with pytest.raises(ValueError):
exact_matching(m.matching_graph, z)
g.nodes[0]['is_boundary'] = True
m2 = Matching(g)
local_matching(m2.matching_graph, np.array([1]))
exact_matching(m2.matching_graph, np.array([1]))
for z in (np.arange(6), np.array([3])):
with pytest.raises(ValueError):
local_matching(m2.matching_graph, z)
with pytest.raises(ValueError):
exact_matching(m2.matching_graph, z)
g.nodes[4]['is_boundary'] = True
m3 = Matching(g)
for z in (np.array([0]), np.arange(6), np.array([3]), np.array([1, 3])):
local_matching(m3.matching_graph, z)
exact_matching(m3.matching_graph, z)
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_negative_weight_raises_value_error():
g = nx.Graph()
g.add_edge(0,1,weight=-1)
with pytest.raises(ValueError):
Matching(g)
with pytest.raises(ValueError):
Matching(csr_matrix([[1,1,0],[0,1,1]]), spacelike_weights=np.array([1,1,-1]))
def test_mwmpm_from_networkx():
g = nx.Graph()
g.add_edge(0, 1, qubit_id=0)
g.add_edge(0, 2, qubit_id=1)
g.add_edge(1, 2, qubit_id=2)
m = Matching(g)
assert(isinstance(m.stabiliser_graph, WeightedStabiliserGraph))
assert(m.num_stabilisers == 3)
assert(m.num_qubits == 3)
assert(m.stabiliser_graph.distance(0,2) == 1)
assert(m.stabiliser_graph.shortest_path(0,2) == [0,2])
g = nx.Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
m = Matching(g)
assert(isinstance(m.stabiliser_graph, WeightedStabiliserGraph))
assert(m.num_stabilisers == 3)
assert(m.num_qubits == 0)
assert(m.stabiliser_graph.distance(0,2) == 1)
assert(m.stabiliser_graph.shortest_path(0,2) == [0,2])
g = nx.Graph()
g.add_edge(0, 1, weight=1.5)
g.add_edge(0, 2, weight=1.7)
g.add_edge(1, 2, weight=1.2)
m = Matching(g)
assert(isinstance(m.stabiliser_graph, WeightedStabiliserGraph))
assert(m.num_stabilisers == 3)
assert(m.num_qubits == 0)
assert(m.stabiliser_graph.distance(0,2) == pytest.approx(1.7))
assert(m.stabiliser_graph.shortest_path(0,2) == [0,2])
def test_load_from_networkx_raises_value_error_if_qubit_id_and_fault_ids_both_supplied():
with pytest.raises(ValueError):
g = nx.Graph()
g.add_edge(0, 1, qubit_id=0, fault_ids=0)
g.add_edge(1, 2, qubit_id=1, fault_ids=1)
m = Matching()
m.load_from_networkx(g)
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_precompute_shortest_paths():
g = nx.Graph()
g.add_edge(0, 1, qubit_id=0)
g.add_edge(1, 2, qubit_id=1)
m = Matching(g)
assert not m.stabiliser_graph.has_computed_all_pairs_shortest_paths()
m2 = Matching(g, precompute_shortest_paths=True)
assert m2.stabiliser_graph.has_computed_all_pairs_shortest_paths()
def test_precompute_shortest_paths():
g = nx.Graph()
g.add_edge(0, 1, fault_ids=0)
g.add_edge(1, 2, fault_ids=1)
m = Matching(g)
assert not m.matching_graph.has_computed_all_pairs_shortest_paths()
m2 = Matching(g, precompute_shortest_paths=True)
assert m2.matching_graph.has_computed_all_pairs_shortest_paths()
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_boundary_from_check_matrix():
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)
assert m.boundary == [4]
assert np.array_equal(m.decode(np.array([1,0,0,0])), np.array([1,0,0,0,0]))
assert np.array_equal(m.decode(np.array([0,1,0,0])), np.array([1,1,0,0,0]))
assert np.array_equal(m.decode(np.array([0,1,1,0])), np.array([0,0,1,0,0]))
assert np.array_equal(m.decode(np.array([0,0,1,0])), np.array([0,0,0,1,1]))
def test_bad_qubit_id_raises_value_error():
g = nx.Graph()
g.add_edge(0,1, qubit_id='test')
with pytest.raises(ValueError):
m = Matching(g)
g = nx.Graph()
g.add_edge(0,1, qubit_id=[[1],[2]])
with pytest.raises(ValueError):
m = Matching(g)
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_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_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_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_draw_matching():
g = nx.Graph()
g.add_edge(0, 1, qubit_id={0}, weight=1.1, error_probability=0.1)
g.add_edge(1, 2, qubit_id={1}, weight=2.1, error_probability=0.2)
g.add_edge(2, 3, qubit_id={2,3}, weight=0.9, error_probability=0.3)
g.nodes[0]['is_boundary'] = True
g.nodes[3]['is_boundary'] = True
g.add_edge(0, 3, weight=0.0)
m = Matching(g)
plt.figure()
m.draw()
def test_repr():
g = nx.Graph()
g.add_edge(0, 1, fault_ids=0)
g.add_edge(1, 2, fault_ids=1)
g.add_edge(2, 3, fault_ids=2)
g.nodes[0]['is_boundary'] = True
g.nodes[3]['is_boundary'] = True
g.add_edge(0, 3, weight=0.0)
m = Matching(g)
assert m.__repr__() == ("<pymatching.Matching object with "
"2 detectors, 2 boundary nodes, and 4 edges>")
def test_repr():
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.nodes[0]['is_boundary'] = True
g.nodes[3]['is_boundary'] = True
g.add_edge(0, 3, weight=0.0)
m = Matching(g)
assert m.__repr__() == ("<pymatching.Matching object with 3 qubits, "
"2 stabilisers, 2 boundary nodes, and 4 edges>")
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_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_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_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_boundary_from_networkx():
g = nx.Graph()
g.add_edge(4,0, qubit_id=0)
g.add_edge(0,1, qubit_id=1)
g.add_edge(1,2, qubit_id=2)
g.add_edge(2,3, qubit_id=3)
g.add_edge(3,4, qubit_id=4)
g.nodes()[4]['is_boundary'] = True
m = Matching(g)
assert m.boundary == [4]
assert np.array_equal(m.decode(np.array([1,0,0,0])), np.array([1,0,0,0,0]))
assert np.array_equal(m.decode(np.array([0,1,0,0])), np.array([1,1,0,0,0]))
assert np.array_equal(m.decode(np.array([0,1,1,0])), np.array([0,0,1,0,0]))
assert np.array_equal(m.decode(np.array([0,0,1,0])), np.array([0,0,0,1,1]))
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
