def test_qaoa_initial_point(self, w, solutions, init_pt):
""" Check first parameter value used is initial point as expected """
optimizer = COBYLA()
qubit_op, _ = max_cut.get_operator(w)
first_pt = []
def cb_callback(eval_count, parameters, mean, std):
nonlocal first_pt
if eval_count == 1:
first_pt = list(parameters)
quantum_instance = QuantumInstance(
BasicAer.get_backend('statevector_simulator'))
qaoa = QAOA(qubit_op,
optimizer,
initial_point=init_pt,
callback=cb_callback,
quantum_instance=quantum_instance)
result = qaoa.compute_minimum_eigenvalue()
x = sample_most_likely(result.eigenstate)
graph_solution = max_cut.get_graph_solution(x)
if init_pt is None: # If None the preferred initial point of QAOA variational form
init_pt = [0.0,
0.0] # i.e. 0,0 should come through as the first point
with self.subTest('Initial Point'):
self.assertListEqual(init_pt, first_pt)
with self.subTest('Solution'):
self.assertIn(''.join([str(int(i)) for i in graph_solution]),
solutions)
def test_qaoa_initial_point(self, w, solutions, init_pt):
""" Check first parameter value used is initial point as expected """
aqua_globals.random_seed = 10598
optimizer = COBYLA()
qubit_op, _ = max_cut.get_operator(w)
first_pt = []
def cb_callback(eval_count, parameters, mean, std):
nonlocal first_pt
if eval_count == 1:
first_pt = list(parameters)
quantum_instance = QuantumInstance(
BasicAer.get_backend('statevector_simulator'),
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed)
qaoa = QAOA(qubit_op,
optimizer,
initial_point=init_pt,
callback=cb_callback,
quantum_instance=quantum_instance)
result = qaoa.compute_minimum_eigenvalue()
x = sample_most_likely(result.eigenstate)
graph_solution = max_cut.get_graph_solution(x)
with self.subTest('Initial Point'):
# If None the preferred random initial point of QAOA variational form
if init_pt is None:
np.testing.assert_almost_equal([1.5108, 0.3378],
first_pt,
decimal=4)
else:
self.assertListEqual(init_pt, first_pt)
with self.subTest('Solution'):
self.assertIn(''.join([str(int(i)) for i in graph_solution]),
solutions)
print("Number of Qubits: %s" % qubit_op.num_qubits)
aqua_globals.random_seed = 10598
optimizer = COBYLA(maxiter=1, tol=1e3)
backend = Aer.get_backend('qasm_simulator')
backend.set_options(method=method)
backend.set_options(max_memory_mb=307200)
#backend.set_options(fusion_enable=False) # TODO: Make this an option
qaoa = QAOA(qubit_op, optimizer, quantum_instance=backend)
#circ = qaoa.construct_circuit([0.0, 0.0])
#qobject = assemble(circ)
#qobject.to_dict()
#with open("qaoa.job", 'w') as f:
# f.write(json.dumps(qobject.to_dict()))
#
#
#backend_options = {'method': 'statevector_gpu'}
#with open("qaoa.config", 'w') as f:
# f.write(json.dumps(backend_options))
start = time.time()
result = qaoa.compute_minimum_eigenvalue()
end = time.time()
x = sample_most_likely(result.eigenstate)
ising_sol = graph_partition.get_graph_solution(x)
print(ising_sol)
print(f'Objective value computed by QAOA is {graph_partition.objective_value(x, w)}')
print("Execution time: %s" % (end - start))