def test_qaoa(self, w, prob, m, solutions):
""" QAOA test """
seed = 0
aqua_globals.random_seed = seed
self.log.debug('Testing %s-step QAOA with MaxCut on graph\n%s', prob,
w)
backend = BasicAer.get_backend('statevector_simulator')
optimizer = COBYLA()
qubit_op, offset = max_cut.get_operator(w)
qaoa = QAOA(qubit_op, optimizer, prob, mixer=m)
quantum_instance = QuantumInstance(backend,
seed_simulator=seed,
seed_transpiler=seed)
result = qaoa.run(quantum_instance)
x = sample_most_likely(result['eigvecs'][0])
graph_solution = max_cut.get_graph_solution(x)
self.log.debug('energy: %s', result['energy'])
self.log.debug('time: %s', result['eval_time'])
self.log.debug('maxcut objective: %s', result['energy'] + offset)
self.log.debug('solution: %s', graph_solution)
self.log.debug('solution objective: %s', max_cut.max_cut_value(x, w))
self.assertIn(''.join([str(int(i)) for i in graph_solution]),
solutions)
def run_simulation(self, backend):
#
# Random 3-regular graph with 12 nodes
#
n = int(os.environ.get("N", "4"))
graph = nx.random_regular_graph(3, n)
for e in graph.edges():
graph[e[0]][e[1]]['weight'] = 1.0
# Compute the weight matrix from the graph
w = np.zeros([n, n])
for i in range(n):
for j in range(n):
temp = graph.get_edge_data(i, j, default=0)
if temp != 0:
w[i, j] = temp["weight"]
# Create an Ising Hamiltonian with docplex.
mdl = Model(name="max_cut")
mdl.node_vars = mdl.binary_var_list(list(range(n)), name="node")
maxcut_func = mdl.sum(w[i, j] * mdl.node_vars[i] *
(1 - mdl.node_vars[j]) for i in range(n)
for j in range(n))
mdl.maximize(maxcut_func)
qubit_op, offset = docplex.get_operator(mdl)
# Run quantum algorithm QAOA on qasm simulator
seed = int(os.environ.get("SEED", "40598"))
aqua_globals.random_seed = seed
spsa = SPSA(max_trials=250)
qaoa = QAOA(qubit_op, spsa, p=5, max_evals_grouped=4)
quantum_instance = QuantumInstance(backend,
shots=1024,
seed_simulator=seed,
seed_transpiler=seed,
optimization_level=0)
result = qaoa.run(quantum_instance)
x = sample_most_likely(result["eigvecs"][0])
result["solution"] = max_cut.get_graph_solution(x)
result["solution_objective"] = max_cut.max_cut_value(x, w)
result["maxcut_objective"] = result["energy"] + offset
"""
print("energy:", result["energy"])
print("time:", result["eval_time"])
print("max-cut objective:", result["energy"] + offset)
print("solution:", max_cut.get_graph_solution(x))
print("solution objective:", max_cut.max_cut_value(x, w))
"""
return result
def test_cplex_ising(self):
""" cplex ising test """
try:
algo = CPLEX_Ising(self.qubit_op, display=0)
result = algo.run()
self.assertEqual(result['energy'], -20.5)
x_dict = result['x_sol']
x = np.array([x_dict[i] for i in sorted(x_dict.keys())])
np.testing.assert_array_equal(max_cut.get_graph_solution(x),
[1, 0, 1, 1])
self.assertEqual(max_cut.max_cut_value(x, self.w), 24)
except AquaError as ex:
self.skipTest(str(ex))
def test_cplex_ising_via_run_algorithm(self):
""" CPlex ising via run algorithm test """
try:
params = {
'problem': {'name': 'ising'},
'algorithm': {'name': 'CPLEX.Ising', 'display': 0}
}
result = run_algorithm(params, self.algo_input)
self.assertEqual(result['energy'], -20.5)
x_dict = result['x_sol']
x = np.array([x_dict[i] for i in sorted(x_dict.keys())])
np.testing.assert_array_equal(
max_cut.get_graph_solution(x), [1, 0, 1, 1])
self.assertEqual(max_cut.max_cut_value(x, self.w), 24)
except AquaError as ex:
self.skipTest(str(ex))
def test_readme_sample(self):
""" readme sample test """
# pylint: disable=import-outside-toplevel,redefined-builtin
def print(*args):
""" overloads print to log values """
if args:
self.log.debug(args[0], *args[1:])
# --- Exact copy of sample code ----------------------------------------
import networkx as nx
import numpy as np
from docplex.mp.model import Model
from qiskit import BasicAer
from qiskit.aqua import aqua_globals, QuantumInstance
from qiskit.aqua.algorithms import QAOA
from qiskit.aqua.components.optimizers import SPSA
from qiskit.optimization.ising import docplex, max_cut
from qiskit.optimization.ising.common import sample_most_likely
# Generate a graph of 4 nodes
n = 4
graph = nx.Graph()
graph.add_nodes_from(np.arange(0, n, 1))
elist = [(0, 1, 1.0), (0, 2, 1.0), (0, 3, 1.0), (1, 2, 1.0), (2, 3, 1.0)]
graph.add_weighted_edges_from(elist)
# Compute the weight matrix from the graph
w = np.zeros([n, n])
for i in range(n):
for j in range(n):
temp = graph.get_edge_data(i, j, default=0)
if temp != 0:
w[i, j] = temp['weight']
# Create an Ising Hamiltonian with docplex.
mdl = Model(name='max_cut')
mdl.node_vars = mdl.binary_var_list(list(range(n)), name='node')
maxcut_func = mdl.sum(w[i, j] * mdl.node_vars[i] * (1 - mdl.node_vars[j])
for i in range(n) for j in range(n))
mdl.maximize(maxcut_func)
qubit_op, offset = docplex.get_operator(mdl)
# Run quantum algorithm QAOA on qasm simulator
seed = 40598
aqua_globals.random_seed = seed
spsa = SPSA(max_trials=250)
qaoa = QAOA(qubit_op, spsa, p=5)
backend = BasicAer.get_backend('qasm_simulator')
quantum_instance = QuantumInstance(backend, shots=1024, seed_simulator=seed,
seed_transpiler=seed)
result = qaoa.run(quantum_instance)
x = sample_most_likely(result.eigenstate)
print('energy:', result.eigenvalue.real)
print('time:', result.optimizer_time)
print('max-cut objective:', result.eigenvalue.real + offset)
print('solution:', max_cut.get_graph_solution(x))
print('solution objective:', max_cut.max_cut_value(x, w))
# ----------------------------------------------------------------------
self.assertListEqual(max_cut.get_graph_solution(x).tolist(), [1, 0, 1, 0])
self.assertAlmostEqual(max_cut.max_cut_value(x, w), 4.0)
temp = graph.get_edge_data(i, j, default=0)
if temp != 0:
w[i, j] = temp['weight']
# Create an Ising Hamiltonian with docplex.
mdl = Model(name='max_cut')
mdl.node_vars = mdl.binary_var_list(list(range(n)), name='node')
maxcut_func = mdl.sum(w[i, j] * mdl.node_vars[i] * (1 - mdl.node_vars[j])
for i in range(n) for j in range(n))
mdl.maximize(maxcut_func)
qubit_op, offset = docplex.get_operator(mdl)
# Run quantum algorithm QAOA on qasm simulator
seed = 40598
aqua_globals.random_seed = seed
spsa = SPSA(max_trials=250)
qaoa = QAOA(qubit_op, spsa, p=2)
backend = BasicAer.get_backend('qasm_simulator')
quantum_instance = QuantumInstance(backend, shots=1024, seed_simulator=seed,
seed_transpiler=seed)
result = qaoa.run(quantum_instance)
parameters=qaoa.optimal_params
x = sample_most_likely(result['eigvecs'][0])
print('energy:', result['energy'])
print('time:', result['eval_time'])
print('max-cut objective:', result['energy'] + offset)
print('solution:', max_cut.get_graph_solution(x))
print('solution objective:', max_cut.max_cut_value(x, w))
print('optimal angles:', parameters)
