def test_graph_partition_vqe(self):
algorithm_cfg = {
'name': 'VQE',
'operator_mode': 'matrix',
'batch_mode': True
}
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 = get_aer_backend('statevector_simulator')
result = run_algorithm(params, self.algo_input, backend=backend)
x = graphpartition.sample_most_likely(result['eigvecs'][0])
# check against the oracle
ising_sol = graphpartition.get_graph_solution(x)
np.testing.assert_array_equal(ising_sol, [1, 0, 0, 1])
oracle = self.brute_force()
self.assertEqual(graphpartition.objective_value(x, self.w), oracle)
def test_graph_partition_direct(self):
algo = ExactEigensolver(self.algo_input.qubit_op,
k=1,
aux_operators=[])
result = algo.run()
x = graphpartition.sample_most_likely(result['eigvecs'][0])
# check against the oracle
ising_sol = graphpartition.get_graph_solution(x)
np.testing.assert_array_equal(ising_sol, [0, 1, 0, 1])
oracle = self.brute_force()
self.assertEqual(graphpartition.objective_value(x, self.w), oracle)
def test_graph_partition(self):
params = {
'problem': {
'name': 'ising'
},
'algorithm': {
'name': 'ExactEigensolver'
}
}
result = run_algorithm(params, self.algo_input)
x = graphpartition.sample_most_likely(result['eigvecs'][0])
# check against the oracle
ising_sol = graphpartition.get_graph_solution(x)
np.testing.assert_array_equal(ising_sol, [0, 1, 0, 1])
oracle = self.brute_force()
self.assertEqual(graphpartition.objective_value(x, self.w), oracle)
def brute_force(self):
# use the brute-force way to generate the oracle
def bitfield(n, L):
result = np.binary_repr(n, L)
return [int(digit)
for digit in result] # [2:] to chop off the "0b" part
L = self.num_nodes
max = 2**L
minimal_v = np.inf
for i in range(max):
cur = bitfield(i, L)
how_many_nonzero = np.count_nonzero(cur)
if how_many_nonzero * 2 != L: # not balanced
continue
cur_v = graphpartition.objective_value(np.array(cur), self.w)
if cur_v < minimal_v:
minimal_v = cur_v
return minimal_v
