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)
def test_variational_study_save_load():
datadir = 'tmp_yulXPXnMBrxeUVt7kYVw'
study_name = 'test_study'
study = ExampleStudy(
study_name,
test_ansatz,
datadir=datadir)
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.results) == 1
result = loaded_study.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)
