def test_cplex_ising_direct(self):
algo = get_algorithm_instance('CPLEX.Ising')
algo.init_args(self.algo_input.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(maxcut.get_graph_solution(x),
[1, 0, 1, 1])
self.assertEqual(maxcut.maxcut_value(x, self.w), 24)
def test_cplex_ising_direct(self):
try:
algo = CPLEX_Ising(self.algo_input.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(maxcut.get_graph_solution(x),
[1, 0, 1, 1])
self.assertEqual(maxcut.maxcut_value(x, self.w), 24)
except AquaError as e:
self.skipTest(str(e))
def test_cplex_ising_via_run_algorithm(self):
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(maxcut.get_graph_solution(x),
[1, 0, 1, 1])
self.assertEqual(maxcut.maxcut_value(x, self.w), 24)
def test_qaoa(self, w, p, solutions):
self.log.debug('Testing {}-step QAOA with MaxCut on graph\n{}'.format(
p, w))
np.random.seed(0)
optimizer = get_optimizer_instance('COBYLA')
qubitOp, offset = maxcut.get_maxcut_qubitops(w)
qaoa = get_algorithm_instance('QAOA.Variational')
qaoa.setup_quantum_backend(backend='statevector_simulator', shots=100)
qaoa.init_args(qubitOp, 'matrix', p, optimizer)
result = qaoa.run()
x = maxcut.sample_most_likely(result['eigvecs'][0])
graph_solution = maxcut.get_graph_solution(x)
self.log.debug('energy: {}'.format(result['energy']))
self.log.debug('time: {}'.format(result['eval_time']))
self.log.debug('maxcut objective: {}'.format(result['energy'] +
offset))
self.log.debug('solution: {}'.format(graph_solution))
self.log.debug('solution objective: {}'.format(
maxcut.maxcut_value(x, w)))
self.assertIn(''.join([str(int(i)) for i in graph_solution]),
solutions)
def test_qaoa(self, w, p, solutions):
self.log.debug('Testing {}-step QAOA with MaxCut on graph\n{}'.format(
p, w))
np.random.seed(0)
backend = Aer.get_backend('statevector_simulator')
optimizer = COBYLA()
qubitOp, offset = maxcut.get_maxcut_qubitops(w)
qaoa = QAOA(qubitOp, optimizer, p, operator_mode='matrix')
quantum_instance = QuantumInstance(backend)
result = qaoa.run(quantum_instance)
x = maxcut.sample_most_likely(result['eigvecs'][0])
graph_solution = maxcut.get_graph_solution(x)
self.log.debug('energy: {}'.format(result['energy']))
self.log.debug('time: {}'.format(result['eval_time']))
self.log.debug('maxcut objective: {}'.format(result['energy'] +
offset))
self.log.debug('solution: {}'.format(graph_solution))
self.log.debug('solution objective: {}'.format(
maxcut.maxcut_value(x, w)))
self.assertIn(''.join([str(int(i)) for i in graph_solution]),
solutions)