def setUp(self):
if 'CPLEX.Ising' not in local_pluggables('algorithm'):
self.skipTest(
'CPLEX.Ising algorithm not found - CPLEX not installed?')
np.random.seed(8123179)
self.w = maxcut.random_graph(4, edge_prob=0.5, weight_range=10)
self.qubit_op, self.offset = maxcut.get_maxcut_qubitops(self.w)
self.algo_input = get_input_instance('EnergyInput')
self.algo_input.qubit_op = self.qubit_op
def set_adj_matrix(self, adj_matrix):
maxcut_op, self.maxcut_shift = maxcut.get_maxcut_qubitops(adj_matrix)
# print("maxcut_op: ", maxcut_op, ", maxcut_shift: ", maxcut_shift)
# TODO: Find different approach of calculating and retrieving diagonal
maxcut_op._paulis_to_matrix()
self.eigenvalues = maxcut_op._dia_matrix
self.calc_expectation_value()
self.draw_expectation_grid()
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)
def setUp(self):
super().setUp()
np.random.seed(8123179)
self.w = maxcut.random_graph(4, edge_prob=0.5, weight_range=10)
self.qubit_op, self.offset = maxcut.get_maxcut_qubitops(self.w)
self.algo_input = EnergyInput(self.qubit_op)
