def test_variational_study_initial_state():
preparation_circuit = cirq.Circuit.from_ops(cirq.X(test_ansatz.qubits[0]))
initial_state = numpy.array([0.0, 0.0, 1.0, 0.0])
class TestObjective(VariationalObjective):
def value(self, circuit_output):
return circuit_output[0].real
study1 = VariationalStudy(
'study1',
test_ansatz,
TestObjective(),
preparation_circuit=preparation_circuit,
black_box_type=variational_black_box.UNITARY_SIMULATE_STATEFUL)
study2 = VariationalStudy(
'study2',
test_ansatz,
TestObjective(),
initial_state=initial_state,
black_box_type=variational_black_box.UNITARY_SIMULATE_STATEFUL)
initial_guess = numpy.random.randn(2)
result1 = study1.optimize(
OptimizationParams(LazyAlgorithm(), initial_guess=initial_guess))
result2 = study2.optimize(
OptimizationParams(LazyAlgorithm(), initial_guess=initial_guess))
numpy.testing.assert_allclose(result1.optimal_value, result2.optimal_value)
def test_variational_study_optimize_and_extend_and_summary():
numpy.random.seed(63351)
study = VariationalStudy(
'study',
test_ansatz,
test_objective,
black_box_type=variational_black_box.XMON_SIMULATE_STATEFUL,
target=-10.5)
assert len(study.trial_results) == 0
assert study.target == -10.5
# Optimization run 1
result = study.optimize(OptimizationParams(test_algorithm), 'run1')
assert len(study.trial_results) == 1
assert isinstance(result, OptimizationTrialResult)
assert result.repetitions == 1
# Extend optimization run 1
study.extend_result('run1', repetitions=2)
assert study.trial_results['run1'].repetitions == 3
# Optimization run 2
study.optimize(OptimizationParams(test_algorithm),
repetitions=2,
use_multiprocessing=True)
result = study.trial_results[0]
assert len(study.trial_results) == 2
assert isinstance(result, OptimizationTrialResult)
assert result.repetitions == 2
# Optimization run 3
study.optimize(OptimizationParams(test_algorithm,
initial_guess=numpy.array([4.5, 8.8]),
initial_guess_array=numpy.array(
[[7.2, 6.3], [3.6, 9.8]]),
cost_of_evaluate=1.0),
reevaluate_final_params=True,
save_x_vals=True)
result = study.trial_results[1]
assert len(study.trial_results) == 3
assert isinstance(result, OptimizationTrialResult)
assert result.repetitions == 1
assert all(result.data_frame['optimal_parameters'].apply(
lambda x: XmonSimulateVariationalBlackBox(test_ansatz, test_objective).
evaluate(x)) == result.data_frame['optimal_value'])
assert isinstance(result.results[0].cost_spent, float)
# Try extending non-existent run
with pytest.raises(KeyError):
study.extend_result('run100')
# Check that getting a summary works
assert str(study).startswith('This study contains')
def test_variational_study_save_load():
datadir = 'tmp_yulXPXnMBrxeUVt7kYVw'
study_name = 'test_study'
study = VariationalStudy(
study_name,
test_ansatz,
test_objective,
initial_state=numpy.array([0.0, 1.0, 0.0,
0.0]).astype(numpy.complex64),
datadir=datadir,
black_box_type=variational_black_box.XMON_SIMULATE_STATEFUL)
study.optimize(
OptimizationParams(ScipyOptimizationAlgorithm(
kwargs={'method': 'COBYLA'}, options={'maxiter': 2}),
initial_guess=numpy.array([7.9, 3.9]),
initial_guess_array=numpy.array([[7.5, 7.6],
[8.8, 1.1]]),
cost_of_evaluate=1.0), 'example')
study.save()
loaded_study = VariationalStudy.load(study_name, datadir=datadir)
assert loaded_study.name == study.name
assert str(loaded_study.circuit) == str(study.circuit)
assert loaded_study.datadir == datadir
assert len(loaded_study.trial_results) == 1
numpy.testing.assert_allclose(loaded_study.initial_state,
numpy.array([0.0, 1.0, 0.0, 0.0]))
result = loaded_study.trial_results['example']
assert isinstance(result, OptimizationTrialResult)
assert result.repetitions == 1
assert isinstance(result.params.algorithm, ScipyOptimizationAlgorithm)
assert result.params.algorithm.kwargs == {'method': 'COBYLA'}
assert result.params.algorithm.options == {'maxiter': 2}
assert result.params.cost_of_evaluate == 1.0
loaded_study = VariationalStudy.load('{}.study'.format(study_name),
datadir=datadir)
assert loaded_study.name == study.name
# Clean up
os.remove(os.path.join(datadir, '{}.study'.format(study_name)))
os.rmdir(datadir)
