def test_measurement_error_mitigation_qaoa(self):
"""measurement error mitigation test with QAOA"""
algorithm_globals.random_seed = 167
# build noise model
noise_model = noise.NoiseModel()
read_err = noise.errors.readout_error.ReadoutError([[0.9, 0.1],
[0.25, 0.75]])
noise_model.add_all_qubit_readout_error(read_err)
backend = Aer.get_backend("aer_simulator")
quantum_instance = QuantumInstance(
backend=backend,
seed_simulator=algorithm_globals.random_seed,
seed_transpiler=algorithm_globals.random_seed,
noise_model=noise_model,
measurement_error_mitigation_cls=CompleteMeasFitter,
)
w = rx.adjacency_matrix(
rx.undirected_gnp_random_graph(5,
0.5,
seed=algorithm_globals.random_seed))
qubit_op, _ = self._get_operator(w)
qaoa = QAOA(
optimizer=COBYLA(maxiter=1),
quantum_instance=quantum_instance,
initial_point=np.asarray([0.0, 0.0]),
)
result = qaoa.compute_minimum_eigenvalue(operator=qubit_op)
self.assertAlmostEqual(result.eigenvalue.real, 3.49, delta=0.05)
def test_graph_empty_dicts(self):
graph = retworkx.undirected_gnp_random_graph(3, 0.9, seed=42)
dot_str = graph.to_dot(lambda _: {}, lambda _: {})
self.assertEqual(
"graph {\n0 ;\n1 ;\n2 ;\n1 -- 0 ;\n2 -- 0 ;\n"
"2 -- 1 ;\n}\n",
dot_str,
)
def test_graph_graph_attrs(self):
graph = retworkx.undirected_gnp_random_graph(3, 0.9, seed=42)
dot_str = graph.to_dot(lambda _: {}, lambda _: {}, {"bgcolor": "red"})
self.assertEqual(
"graph {\nbgcolor=red ;\n0 ;\n1 ;\n2 ;\n1 -- 0 ;\n"
"2 -- 0 ;\n2 -- 1 ;\n}\n",
dot_str,
)
def test_qaoa_random_initial_point(self):
"""QAOA random initial point"""
w = rx.adjacency_matrix(
rx.undirected_gnp_random_graph(5, 0.5, seed=algorithm_globals.random_seed)
)
qubit_op, _ = self._get_operator(w)
qaoa = QAOA(optimizer=NELDER_MEAD(disp=True), reps=1, quantum_instance=self.qasm_simulator)
result = qaoa.compute_minimum_eigenvalue(operator=qubit_op)
self.assertLess(result.eigenvalue, -0.97)
def test_gnp_random_against_networkx_max_cardinality(self):
rx_graph = retworkx.undirected_gnp_random_graph(10, 0.78, seed=428)
nx_graph = networkx.Graph(list(rx_graph.edge_list()))
nx_matches = networkx.max_weight_matching(nx_graph,
maxcardinality=True)
rx_matches = retworkx.max_weight_matching(rx_graph,
max_cardinality=True,
verify_optimum=True)
self.compare_rx_nx_sets(rx_graph, rx_matches, nx_matches, 428,
nx_graph)
def test_gnp_random_against_networkx(self):
for i in range(1024):
# TODO: add back subTest usage on new testtools release
rx_graph = retworkx.undirected_gnp_random_graph(10,
0.75,
seed=42 + i)
nx_graph = networkx.Graph(list(rx_graph.edge_list()))
nx_matches = networkx.max_weight_matching(nx_graph)
rx_matches = retworkx.max_weight_matching(rx_graph,
verify_optimum=True)
self.compare_rx_nx_sets(rx_graph, rx_matches, nx_matches, 42 + i,
nx_graph)
def test_gnp_random_against_networkx(self):
for i in range(1024):
with self.subTest(i=i):
rx_graph = retworkx.undirected_gnp_random_graph(10,
0.75,
seed=42 + i)
nx_graph = networkx.Graph(list(rx_graph.edge_list()))
nx_matches = networkx.max_weight_matching(nx_graph)
rx_matches = retworkx.max_weight_matching(rx_graph,
verify_optimum=True)
self.compare_rx_nx_sets(rx_graph, rx_matches, nx_matches,
42 + i, nx_graph)
def test_qaoa_random_initial_point(self):
""" QAOA random initial point """
w = rx.adjacency_matrix(
rx.undirected_gnp_random_graph(5,
0.5,
seed=algorithm_globals.random_seed))
qubit_op, _ = self._get_operator(w)
qaoa = QAOA(optimizer=NELDER_MEAD(disp=True),
reps=1,
quantum_instance=self.qasm_simulator)
_ = qaoa.compute_minimum_eigenvalue(operator=qubit_op)
np.testing.assert_almost_equal([-0.560, 0.315],
qaoa.optimal_params,
decimal=3)
def test_gnp_random_against_networkx_with_negative_weight(self):
for i in range(1024):
with self.subTest(i=i):
random.seed(i)
rx_graph = retworkx.undirected_gnp_random_graph(10,
0.75,
seed=42 + i)
for edge in rx_graph.edge_list():
rx_graph.update_edge(*edge, random.randint(-5000, 5000))
nx_graph = networkx.Graph([(x[0], x[1], {
"weight": x[2]
}) for x in rx_graph.weighted_edge_list()])
nx_matches = networkx.max_weight_matching(nx_graph)
rx_matches = retworkx.max_weight_matching(
rx_graph, weight_fn=lambda x: x, verify_optimum=True)
self.compare_rx_nx_sets(rx_graph, rx_matches, nx_matches,
42 + i, nx_graph)
def test_gnp_random_against_networkx_with_weight(self):
for i in range(1024):
# TODO: add back subTest usage on new testtools release
random.seed(i)
rx_graph = retworkx.undirected_gnp_random_graph(10,
.75,
seed=42 + i)
for edge in rx_graph.edge_list():
rx_graph.update_edge(*edge, random.randint(0, 5000))
nx_graph = networkx.Graph([(x[0], x[1], {
'weight': x[2]
}) for x in rx_graph.weighted_edge_list()])
nx_matches = networkx.max_weight_matching(nx_graph)
rx_matches = retworkx.max_weight_matching(rx_graph,
weight_fn=lambda x: x,
verify_optimum=True)
self.compare_rx_nx_sets(rx_graph, rx_matches, nx_matches, 42 + i,
nx_graph)
def test_random_gnp_undirected_invalid_probability(self):
with self.assertRaises(ValueError):
retworkx.undirected_gnp_random_graph(23, 123.5)
def test_random_gnp_undirected_invalid_num_nodes(self):
with self.assertRaises(ValueError):
retworkx.undirected_gnp_random_graph(-23, .5)
def test_random_gnp_undirected_complete_graph(self):
graph = retworkx.undirected_gnp_random_graph(20, 1)
self.assertEqual(len(graph), 20)
self.assertEqual(len(graph.edges()), 20 * (20 - 1) / 2)
def test_random_gnp_undirected_empty_graph(self):
graph = retworkx.undirected_gnp_random_graph(20, 0)
self.assertEqual(len(graph), 20)
self.assertEqual(len(graph.edges()), 0)
def test_random_gnp_undirected(self):
graph = retworkx.undirected_gnp_random_graph(20, .5, seed=10)
self.assertEqual(len(graph), 20)
self.assertEqual(len(graph.edges()), 105)
def test_random_graph_full_path(self):
graph = retworkx.undirected_gnp_random_graph(100, 0.95, seed=42)
adjacency_matrix = retworkx.graph_adjacency_matrix(graph)
new_graph = retworkx.PyGraph.from_adjacency_matrix(adjacency_matrix)
new_adjacency_matrix = retworkx.graph_adjacency_matrix(new_graph)
self.assertTrue(np.array_equal(adjacency_matrix, new_adjacency_matrix))
def setUp(self):
super().setUp()
if self.class_type == "PyGraph":
self.graph = retworkx.undirected_gnp_random_graph(10, 0.5, seed=42)
else:
self.graph = retworkx.directed_gnp_random_graph(10, 0.5, seed=42)
for i in range(log_m):
width = initial_width * pow(2, i)
for u, v in nx.bfs_beam_edges(a, s, v, width):
if con(v):
return v
print(nx.NodeNotFound("no node"))
def has_high_centrality(v: int) -> bool:
return centrality[v] >= avg_centrality
seed: int = 89
# conversion of retworkx graph to networkx couldn't find any func in retworkx to calculate centrality
G: rx.PyGraph = rx.undirected_gnp_random_graph(100, 0.5, seed=seed)
n1: numpy.ndarray = rx.graph_adjacency_matrix(G, None)
G1: nx.classes.graph.Graph = nx.from_numpy_matrix(n1)
centrality: dict = nx.eigenvector_centrality(G1)
avg_centrality: float = sum(centrality.values()) / len(G1)
source: int = 0
value = centrality.get
print(type(value))
condition: Callable[[int], bool] = has_high_centrality
found_node: int = progressive_widening_search(G1, source, value, condition)
c: float = centrality[found_node]
print(f"found node {found_node} with centrality {c}")
# plotting
def test_graph_no_args(self):
graph = retworkx.undirected_gnp_random_graph(3, .95, seed=24)
dot_str = graph.to_dot()
self.assertEqual("graph {\n0 ;\n1 ;\n2 ;\n2 -- 0 ;\n2 -- 1 ;\n}\n",
dot_str)
