def test_saving_and_loading(self):
backend = BasicAer.get_backend('statevector_simulator')
num_qubits = self.algo_input.qubit_op.num_qubits
init_state = Zero(num_qubits)
var_form = RY(num_qubits, 3, initial_state=init_state)
optimizer = L_BFGS_B()
algo = VQE(self.algo_input.qubit_op, var_form, optimizer, 'matrix')
fd, cache_tmp_file = tempfile.mkstemp(suffix='.inp')
os.close(fd)
circuit_cache = CircuitCache(skip_qobj_deepcopy=True, cache_file=cache_tmp_file)
quantum_instance_caching = QuantumInstance(backend, circuit_cache=circuit_cache, skip_qobj_validation=True)
result_caching = algo.run(quantum_instance_caching)
self.assertLessEqual(circuit_cache.misses, 0)
is_file_exist = os.path.exists(cache_tmp_file)
self.assertTrue(is_file_exist, "Does not store content successfully.")
circuit_cache_new = CircuitCache(skip_qobj_deepcopy=True, cache_file=cache_tmp_file)
self.assertEqual(circuit_cache.mappings, circuit_cache_new.mappings)
self.assertLessEqual(circuit_cache_new.misses, 0)
if is_file_exist:
os.remove(cache_tmp_file)
def test_saving_and_loading_e2e(self):
backend = BasicAer.get_backend('statevector_simulator')
num_qubits = self.algo_input.qubit_op.num_qubits
init_state = Zero(num_qubits)
var_form = RY(num_qubits, 3, initial_state=init_state)
optimizer = L_BFGS_B()
algo = VQE(self.algo_input.qubit_op, var_form, optimizer, 'matrix')
with tempfile.NamedTemporaryFile(suffix='.inp',
delete=True) as cache_tmp_file:
cache_tmp_file_name = cache_tmp_file.name
quantum_instance_caching = QuantumInstance(
backend,
circuit_caching=True,
cache_file=cache_tmp_file_name,
skip_qobj_deepcopy=True,
skip_qobj_validation=True)
algo.run(quantum_instance_caching)
self.assertLessEqual(quantum_instance_caching.circuit_cache.misses,
0)
is_file_exist = os.path.exists(cache_tmp_file_name)
self.assertTrue(is_file_exist,
"Does not store content successfully.")
circuit_cache_new = CircuitCache(skip_qobj_deepcopy=True,
cache_file=cache_tmp_file_name)
self.assertEqual(quantum_instance_caching.circuit_cache.mappings,
circuit_cache_new.mappings)
self.assertLessEqual(circuit_cache_new.misses, 0)
def test_vqe_caching_direct(self, batch_mode=True):
backend = BasicAer.get_backend('statevector_simulator')
num_qubits = self.algo_input.qubit_op.num_qubits
init_state = Zero(num_qubits)
var_form = RY(num_qubits, 3, initial_state=init_state)
optimizer = L_BFGS_B()
algo = VQE(self.algo_input.qubit_op, var_form, optimizer, 'matrix', batch_mode=batch_mode)
circuit_cache = CircuitCache(skip_qobj_deepcopy=True)
quantum_instance_caching = QuantumInstance(backend, circuit_cache=circuit_cache, skip_qobj_validation=True)
result_caching = algo.run(quantum_instance_caching)
self.assertLessEqual(circuit_cache.misses, 0)
self.assertAlmostEqual(self.reference_vqe_result['statevector_simulator']['energy'], result_caching['energy'])
speedup_check = 3
self.log.info(result_caching['eval_time'],
self.reference_vqe_result['statevector_simulator']['eval_time']/speedup_check)
def build_algorithm_from_dict(params, algo_input=None, backend=None):
"""
Construct algorithm as named in params, using params and algo_input as input data
and returning a QuantumAlgorithm and QuantumInstance instance
Args:
params (dict): Dictionary of params for algo and dependent objects
algo_input (AlgorithmInput): Main input data for algorithm. Optional, an algo may run entirely from params
backend (BaseBackend): Backend object to be used in place of backend name
Returns:
Ready-to-run QuantumAlgorithm and QuantumInstance as specified in input parameters. Note that
no QuantumInstance will be returned if none is specified - None will be returned instead.
"""
_discover_on_demand()
inputparser = InputParser(params)
inputparser.parse()
# before merging defaults attempts to find a provider for the backend in case no
# provider was passed
if backend is None and inputparser.get_section_property(
JSONSchema.BACKEND, JSONSchema.PROVIDER) is None:
backend_name = inputparser.get_section_property(
JSONSchema.BACKEND, JSONSchema.NAME)
if backend_name is not None:
inputparser.set_section_property(
JSONSchema.BACKEND, JSONSchema.PROVIDER,
get_provider_from_backend(backend_name))
inputparser.validate_merge_defaults()
logger.debug('Algorithm Input: {}'.format(
json.dumps(inputparser.get_sections(), sort_keys=True, indent=4)))
algo_name = inputparser.get_section_property(PluggableType.ALGORITHM.value,
JSONSchema.NAME)
if algo_name is None:
raise AquaError('Missing algorithm name')
if algo_name not in local_pluggables(PluggableType.ALGORITHM):
raise AquaError(
'Algorithm "{0}" missing in local algorithms'.format(algo_name))
if algo_input is None:
input_name = inputparser.get_section_property('input', JSONSchema.NAME)
if input_name is not None:
input_params = copy.deepcopy(
inputparser.get_section_properties('input'))
del input_params[JSONSchema.NAME]
convert_json_to_dict(input_params)
algo_input = get_pluggable_class(
PluggableType.INPUT, input_name).from_params(input_params)
algo_params = copy.deepcopy(inputparser.get_sections())
algorithm = get_pluggable_class(PluggableType.ALGORITHM,
algo_name).init_params(
algo_params, algo_input)
random_seed = inputparser.get_section_property(JSONSchema.PROBLEM,
'random_seed')
algorithm.random_seed = random_seed
quantum_instance = None
# setup backend
backend_provider = inputparser.get_section_property(
JSONSchema.BACKEND, JSONSchema.PROVIDER)
backend_name = inputparser.get_section_property(JSONSchema.BACKEND,
JSONSchema.NAME)
if backend_provider is not None and backend_name is not None: # quantum algorithm
backend_cfg = {
k: v
for k, v in inputparser.get_section(JSONSchema.BACKEND).items()
if k not in [JSONSchema.PROVIDER, JSONSchema.NAME]
}
# TODO, how to build the noise model from a dictionary?
backend_cfg['seed_mapper'] = random_seed
pass_manager = PassManager() if backend_cfg.pop(
'skip_transpiler', False) else None
if pass_manager is not None:
backend_cfg['pass_manager'] = pass_manager
if backend is None or not isinstance(backend, BaseBackend):
backend = get_backend_from_provider(backend_provider, backend_name)
backend_cfg['backend'] = backend
# overwrite the basis_gates and coupling_map
basis_gates = backend_cfg.pop('basis_gates', None)
coupling_map = backend_cfg.pop('coupling_map', None)
if backend.configuration().simulator:
if basis_gates is not None:
backend.configuration().basis_gates = basis_gates
if coupling_map is not None:
backend.configuration().coupling_map = coupling_map
else:
logger.warning(
"Change basis_gates and coupling_map on a real device is disallowed."
)
shots = backend_cfg.pop('shots', 1024)
seed = random_seed
max_credits = backend_cfg.pop('max_credits', 10)
memory = backend_cfg.pop('memory', False)
run_config = RunConfig(shots=shots,
max_credits=max_credits,
memory=memory)
if seed is not None:
run_config.seed = seed
backend_cfg['run_config'] = run_config
backend_cfg['skip_qobj_validation'] = inputparser.get_section_property(
JSONSchema.PROBLEM, 'skip_qobj_validation')
use_caching = inputparser.get_section_property(JSONSchema.PROBLEM,
'circuit_caching')
if use_caching:
deepcopy_qobj = inputparser.get_section_property(
JSONSchema.PROBLEM, 'skip_qobj_deepcopy')
cache_file = inputparser.get_section_property(
JSONSchema.PROBLEM, 'circuit_cache_file')
backend_cfg['circuit_cache'] = CircuitCache(
skip_qobj_deepcopy=deepcopy_qobj, cache_file=cache_file)
quantum_instance = QuantumInstance(**backend_cfg)
# Note that quantum_instance can be None if none is specified
return algorithm, quantum_instance
def _build_algorithm_from_dict(self, quantum_instance):
_discover_on_demand()
self._parser = InputParser(self._params)
self._parser.parse()
# before merging defaults attempts to find a provider for the backend in case no
# provider was passed
if quantum_instance is None and self._parser.get_section_property(
JSONSchema.BACKEND, JSONSchema.PROVIDER) is None:
backend_name = self._parser.get_section_property(
JSONSchema.BACKEND, JSONSchema.NAME)
if backend_name is not None:
self._parser.set_section_property(
JSONSchema.BACKEND, JSONSchema.PROVIDER,
get_provider_from_backend(backend_name))
self._parser.validate_merge_defaults()
logger.debug('Algorithm Input: {}'.format(
json.dumps(self._parser.get_sections(), sort_keys=True, indent=4)))
algo_name = self._parser.get_section_property(
PluggableType.ALGORITHM.value, JSONSchema.NAME)
if algo_name is None:
raise AquaError('Missing algorithm name')
if algo_name not in local_pluggables(PluggableType.ALGORITHM):
raise AquaError(
'Algorithm "{0}" missing in local algorithms'.format(
algo_name))
if self._algorithm_input is None:
input_name = self._parser.get_section_property(
'input', JSONSchema.NAME)
if input_name is not None:
input_params = copy.deepcopy(
self._parser.get_section_properties('input'))
del input_params[JSONSchema.NAME]
convert_json_to_dict(input_params)
self._algorithm_input = get_pluggable_class(
PluggableType.INPUT, input_name).from_params(input_params)
algo_params = copy.deepcopy(self._parser.get_sections())
self._quantum_algorithm = get_pluggable_class(
PluggableType.ALGORITHM,
algo_name).init_params(algo_params, self._algorithm_input)
random_seed = self._parser.get_section_property(
JSONSchema.PROBLEM, 'random_seed')
self._quantum_algorithm.random_seed = random_seed
if isinstance(quantum_instance, QuantumInstance):
self._quantum_instance = quantum_instance
return
backend = None
if isinstance(quantum_instance, BaseBackend):
backend = quantum_instance
elif quantum_instance is not None:
raise AquaError(
'Invalid QuantumInstance or BaseBackend parameter {}.'.format(
quantum_instance))
# setup backend
backend_provider = self._parser.get_section_property(
JSONSchema.BACKEND, JSONSchema.PROVIDER)
backend_name = self._parser.get_section_property(
JSONSchema.BACKEND, JSONSchema.NAME)
if backend_provider is not None and backend_name is not None: # quantum algorithm
backend_cfg = {
k: v
for k, v in self._parser.get_section(
JSONSchema.BACKEND).items()
if k not in [JSONSchema.PROVIDER, JSONSchema.NAME]
}
# TODO, how to build the noise model from a dictionary?
backend_cfg['seed_mapper'] = random_seed
pass_manager = PassManager() if backend_cfg.pop(
'skip_transpiler', False) else None
if pass_manager is not None:
backend_cfg['pass_manager'] = pass_manager
if backend is None:
backend = get_backend_from_provider(backend_provider,
backend_name)
backend_cfg['backend'] = backend
# overwrite the basis_gates and coupling_map
basis_gates = backend_cfg.pop('basis_gates', None)
if isinstance(basis_gates, str):
basis_gates = basis_gates.split(',')
coupling_map = backend_cfg.pop('coupling_map', None)
if backend.configuration().simulator:
if basis_gates is not None:
backend.configuration().basis_gates = basis_gates
if coupling_map is not None:
backend.configuration().coupling_map = coupling_map
else:
logger.warning(
"Change basis_gates and coupling_map on a real device is disallowed."
)
shots = backend_cfg.pop('shots', 1024)
seed = random_seed
max_credits = backend_cfg.pop('max_credits', 10)
memory = backend_cfg.pop('memory', False)
run_config = RunConfig(shots=shots,
max_credits=max_credits,
memory=memory)
if seed is not None:
run_config.seed = seed
backend_cfg['run_config'] = run_config
backend_cfg[
'skip_qobj_validation'] = self._parser.get_section_property(
JSONSchema.PROBLEM, 'skip_qobj_validation')
use_caching = self._parser.get_section_property(
JSONSchema.PROBLEM, 'circuit_caching')
if use_caching:
deepcopy_qobj = self._parser.get_section_property(
JSONSchema.PROBLEM, 'skip_qobj_deepcopy')
cache_file = self._parser.get_section_property(
JSONSchema.PROBLEM, 'circuit_cache_file')
backend_cfg['circuit_cache'] = CircuitCache(
skip_qobj_deepcopy=deepcopy_qobj, cache_file=cache_file)
self._quantum_instance = QuantumInstance(**backend_cfg)
