def test_qaoa(self, w, prob, m, solutions):
""" QAOA test """
seed = 0
aqua_globals.random_seed = seed
os.environ.pop('QISKIT_AQUA_CIRCUIT_CACHE', None)
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_qubit_op(w)
qaoa = QAOA(qubit_op, optimizer, prob, mixer=m)
# TODO: cache fails for QAOA since we construct the evolution circuit via instruction
quantum_instance = QuantumInstance(backend,
circuit_caching=False,
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)
if quantum_instance.has_circuit_caching:
self.assertLess(quantum_instance._circuit_cache.misses, 3)
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_qubit_op(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 test_graph_partition_vqe(self):
""" Graph Partition VQE test """
algorithm_cfg = {
'name': 'VQE',
'max_evals_grouped': 2
}
optimizer_cfg = {
'name': 'SPSA',
'max_trials': 300
}
var_form_cfg = {
'name': 'RY',
'depth': 5,
'entanglement': 'linear'
}
params = {
'problem': {'name': 'ising', 'random_seed': 10598},
'algorithm': algorithm_cfg,
'optimizer': optimizer_cfg,
'variational_form': var_form_cfg
}
backend = BasicAer.get_backend('statevector_simulator')
result = run_algorithm(params, self.algo_input, backend=backend)
x = sample_most_likely(result['eigvecs'][0])
# check against the oracle
ising_sol = graph_partition.get_graph_solution(x)
np.testing.assert_array_equal(ising_sol, [0, 1, 0, 1])
oracle = self._brute_force()
self.assertEqual(graph_partition.objective_value(x, self.w), oracle)
def test_clique_vqe(self):
""" VQE Clique test """
algorithm_cfg = {
'name': 'VQE',
'max_evals_grouped': 2
}
optimizer_cfg = {
'name': 'COBYLA'
}
var_form_cfg = {
'name': 'RY',
'depth': 5,
'entanglement': 'linear'
}
params = {
'problem': {'name': 'ising', 'random_seed': 10598},
'algorithm': algorithm_cfg,
'optimizer': optimizer_cfg,
'variational_form': var_form_cfg
}
backend = BasicAer.get_backend('statevector_simulator')
result = run_algorithm(params, self.algo_input, backend=backend)
x = sample_most_likely(result['eigvecs'][0])
ising_sol = clique.get_graph_solution(x)
np.testing.assert_array_equal(ising_sol, [1, 1, 1, 1, 1])
oracle = self._brute_force()
self.assertEqual(clique.satisfy_or_not(ising_sol, self.w, self.k), oracle)
def test_exact_cover_vqe(self):
""" Exact Cover VQE test """
algorithm_cfg = {'name': 'VQE', 'max_evals_grouped': 2}
optimizer_cfg = {'name': 'COBYLA'}
var_form_cfg = {'name': 'RYRZ', 'depth': 5}
params = {
'problem': {
'name': 'ising',
'random_seed': 10598
},
'algorithm': algorithm_cfg,
'optimizer': optimizer_cfg,
'variational_form': var_form_cfg
}
backend = BasicAer.get_backend('statevector_simulator')
result = run_algorithm(params, self.algo_input, backend=backend)
x = sample_most_likely(result['eigvecs'][0])
ising_sol = exact_cover.get_solution(x)
oracle = self._brute_force()
self.assertEqual(
exact_cover.check_solution_satisfiability(ising_sol,
self.list_of_subsets),
oracle)
def test_vertex_cover_vqe(self):
""" Vertex Cover VQE test """
algorithm_cfg = {'name': 'VQE', 'max_evals_grouped': 2}
optimizer_cfg = {'name': 'SPSA', 'max_trials': 200}
var_form_cfg = {
'name': 'RYRZ',
'depth': 3,
}
params = {
'problem': {
'name': 'ising',
'random_seed': self.seed
},
'algorithm': algorithm_cfg,
'optimizer': optimizer_cfg,
'variational_form': var_form_cfg
}
backend = BasicAer.get_backend('qasm_simulator')
result = run_algorithm(params, self.algo_input, backend=backend)
x = sample_most_likely(result['eigvecs'][0])
sol = vertex_cover.get_graph_solution(x)
oracle = self._brute_force()
self.assertEqual(np.count_nonzero(sol), oracle)
def test_set_packing_direct(self):
""" set packing direct test """
algo = ExactEigensolver(self.algo_input.qubit_op, k=1, aux_operators=[])
result = algo.run()
x = sample_most_likely(result['eigvecs'][0])
ising_sol = set_packing.get_solution(x)
np.testing.assert_array_equal(ising_sol, [0, 1, 1])
oracle = self._brute_force()
self.assertEqual(np.count_nonzero(ising_sol), oracle)
def test_clique_direct(self):
""" Clique Direct test """
algo = ExactEigensolver(self.algo_input.qubit_op, k=1, aux_operators=[])
result = algo.run()
x = sample_most_likely(result['eigvecs'][0])
ising_sol = clique.get_graph_solution(x)
np.testing.assert_array_equal(ising_sol, [1, 1, 1, 1, 1])
oracle = self._brute_force()
self.assertEqual(clique.satisfy_or_not(ising_sol, self.w, self.k), oracle)
def test_graph_partition_direct(self):
""" Graph Partition Direct test """
algo = ExactEigensolver(self.algo_input.qubit_op, k=1, aux_operators=[])
result = algo.run()
x = sample_most_likely(result['eigvecs'][0])
# check against the oracle
ising_sol = graph_partition.get_graph_solution(x)
np.testing.assert_array_equal(ising_sol, [0, 1, 0, 1])
oracle = self._brute_force()
self.assertEqual(graph_partition.objective_value(x, self.w), oracle)
def test_vertex_cover_direct(self):
""" Vertex Cover Direct test """
algo = ExactEigensolver(self.algo_input.qubit_op,
k=1,
aux_operators=[])
result = algo.run()
x = sample_most_likely(result['eigvecs'][0])
sol = vertex_cover.get_graph_solution(x)
np.testing.assert_array_equal(sol, [0, 1, 1])
oracle = self._brute_force()
self.assertEqual(np.count_nonzero(sol), oracle)
def test_set_packing(self):
""" set packing test """
params = {
'problem': {'name': 'ising'},
'algorithm': {'name': 'ExactEigensolver'}
}
result = run_algorithm(params, self.algo_input)
x = sample_most_likely(result['eigvecs'][0])
ising_sol = set_packing.get_solution(x)
np.testing.assert_array_equal(ising_sol, [0, 1, 1])
oracle = self._brute_force()
self.assertEqual(np.count_nonzero(ising_sol), oracle)
def test_graph_partition(self):
""" Graph Partition test """
params = {
'problem': {'name': 'ising'},
'algorithm': {'name': 'ExactEigensolver'}
}
result = run_algorithm(params, self.algo_input)
x = sample_most_likely(result['eigvecs'][0])
# check against the oracle
ising_sol = graph_partition.get_graph_solution(x)
np.testing.assert_array_equal(ising_sol, [0, 1, 0, 1])
oracle = self._brute_force()
self.assertEqual(graph_partition.objective_value(x, self.w), oracle)
def test_exact_cover_direct(self):
""" Exact Cover Direct test """
algo = ExactEigensolver(self.algo_input.qubit_op,
k=1,
aux_operators=[])
result = algo.run()
x = sample_most_likely(result['eigvecs'][0])
ising_sol = exact_cover.get_solution(x)
np.testing.assert_array_equal(ising_sol, [0, 1, 1, 0])
oracle = self._brute_force()
self.assertEqual(
exact_cover.check_solution_satisfiability(ising_sol,
self.list_of_subsets),
oracle)
def test_exact_cover(self):
""" Exact Cover test """
params = {
'problem': {
'name': 'ising'
},
'algorithm': {
'name': 'ExactEigensolver'
}
}
result = run_algorithm(params, self.algo_input)
x = sample_most_likely(result['eigvecs'][0])
ising_sol = exact_cover.get_solution(x)
np.testing.assert_array_equal(ising_sol, [0, 1, 1, 0])
oracle = self._brute_force()
self.assertEqual(
exact_cover.check_solution_satisfiability(ising_sol,
self.list_of_subsets),
oracle)
def test_set_packing_vqe(self):
""" set packing vqe test """
try:
from qiskit import Aer
except Exception as ex: # pylint: disable=broad-except
self.skipTest("Aer doesn't appear to be installed. Error: '{}'".format(str(ex)))
return
algorithm_cfg = {
'name': 'VQE',
'max_evals_grouped': 2
}
optimizer_cfg = {
'name': 'SPSA',
'max_trials': 200
}
var_form_cfg = {
'name': 'RY',
'depth': 5,
'entanglement': 'linear'
}
params = {
'problem': {'name': 'ising', 'random_seed': 100},
'algorithm': algorithm_cfg,
'optimizer': optimizer_cfg,
'variational_form': var_form_cfg
}
backend = Aer.get_backend('qasm_simulator')
result = run_algorithm(params, self.algo_input, backend=backend)
x = sample_most_likely(result['eigvecs'][0])
ising_sol = set_packing.get_solution(x)
oracle = self._brute_force()
self.assertEqual(np.count_nonzero(ising_sol), oracle)
