def test_validate(self):
json_dict = self.parser.get_sections()
p = InputParser(json_dict)
p.parse()
try:
p.validate_merge_defaults()
except Exception as e:
self.fail(str(e))
with self.assertRaises(AquaError):
p.set_section_property('backend', 'max_credits', -1)
def test_validate(self):
json_dict = self.parser.get_sections()
p = InputParser(json_dict)
p.parse()
try:
p.validate_merge_defaults()
except Exception as e:
self.fail(str(e))
p.set_section_property('optimizer', 'dummy', 1002)
self.assertRaises(AquaError, p.validate_merge_defaults)
def test_validate(self):
""" validate test """
json_dict = self.parser.get_sections()
parse = InputParser(json_dict)
parse.parse()
try:
parse.validate_merge_defaults()
except Exception as ex: # pylint: disable=broad-except
self.fail(str(ex))
with self.assertRaises(AquaError):
parse.set_section_property('backend', 'max_credits', -1)
def run_algorithm_to_json(params,
algo_input=None,
jsonfile='algorithm.json'):
"""
Run algorithm as named in params.
Using params and algo_input as input data
and save the combined input as a json file. This json is self-contained and
can later be used as a basis to call run_algorithm
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
jsonfile (str): Name of file in which json should be saved
Returns:
Result dictionary containing the jsonfile name
"""
_discover_on_demand()
inputparser = InputParser(params)
inputparser.parse()
inputparser.validate_merge_defaults()
algo_params = copy.deepcopy(inputparser.get_sections())
if algo_input is not None:
input_params = algo_input.to_params()
convert_dict_to_json(input_params)
algo_params['input'] = input_params
algo_params['input']['name'] = algo_input.configuration['name']
logger.debug('Result: {}'.format(
json.dumps(algo_params, sort_keys=True, indent=4)))
with open(jsonfile, 'w') as fp:
json.dump(algo_params, fp, sort_keys=True, indent=4)
logger.info("Algorithm input file saved: '{}'".format(jsonfile))
return {'jsonfile': jsonfile}
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
class QiskitAqua(object):
"""Main Aqua class."""
def __init__(self, params, algo_input=None, quantum_instance=None):
"""
Create an QiskitAqua object
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
quantum_instance (QuantumInstance or BaseBackend): the experiemental settings to be used in place of backend name
"""
self._params = params
self._algorithm_input = algo_input
self._quantum_instance = None
self._quantum_algorithm = None
self._result = {}
self._parser = None
self._build_algorithm_from_dict(quantum_instance)
@property
def params(self):
"""Return Aqua params."""
return self._params
@property
def algorithm_input(self):
"""Return Algorithm Input."""
return self._algorithm_input
@property
def quantum_instance(self):
"""Return Quantum Instance."""
return self._quantum_instance
@property
def quantum_algorithm(self):
"""Return Qusntum Algorithm."""
return self._quantum_algorithm
@property
def result(self):
"""Return Experiment Result."""
return self._result
@property
def json_result(self):
"""Return Experiment Result as JSON."""
return convert_dict_to_json(self.result) if isinstance(
self.result, dict) else None
@property
def parser(self):
"""Return Aqua parser."""
return self._parser
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)
def run(self, json_output=False):
if self.quantum_algorithm is None:
raise AquaError('Missing Quantum Algorithm.')
self._result = self.quantum_algorithm.run(self.quantum_instance)
return self.json_result if json_output else self.result
@staticmethod
def run_algorithm_to_json(params,
algo_input=None,
jsonfile='algorithm.json'):
"""
Run algorithm as named in params.
Using params and algo_input as input data
and save the combined input as a json file. This json is self-contained and
can later be used as a basis to call run_algorithm
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
jsonfile (str): Name of file in which json should be saved
Returns:
Result dictionary containing the jsonfile name
"""
_discover_on_demand()
inputparser = InputParser(params)
inputparser.parse()
inputparser.validate_merge_defaults()
algo_params = copy.deepcopy(inputparser.get_sections())
if algo_input is not None:
input_params = algo_input.to_params()
convert_dict_to_json(input_params)
algo_params['input'] = input_params
algo_params['input']['name'] = algo_input.configuration['name']
logger.debug('Result: {}'.format(
json.dumps(algo_params, sort_keys=True, indent=4)))
with open(jsonfile, 'w') as fp:
json.dump(algo_params, fp, sort_keys=True, indent=4)
logger.info("Algorithm input file saved: '{}'".format(jsonfile))
return {'jsonfile': jsonfile}