class CircuitSampler(ConverterBase):
"""
The CircuitSampler traverses an Operator and converts any CircuitStateFns into
approximations of the state function by a DictStateFn or VectorStateFn using a quantum
backend. Note that in order to approximate the value of the CircuitStateFn, it must 1) send
state function through a depolarizing channel, which will destroy all phase information and
2) replace the sampled frequencies with **square roots** of the frequency, rather than the raw
probability of sampling (which would be the equivalent of sampling the **square** of the
state function, per the Born rule.
The CircuitSampler aggressively caches transpiled circuits to handle re-parameterization of
the same circuit efficiently. If you are converting multiple different Operators,
you are better off using a different CircuitSampler for each Operator to avoid cache thrashing.
"""
def __init__(self,
backend: Union[BaseBackend, QuantumInstance] = None,
statevector: Optional[bool] = None,
param_qobj: bool = False,
attach_results: bool = False) -> None:
"""
Args:
backend: The quantum backend or QuantumInstance to use to sample the circuits.
statevector: If backend is a statevector backend, whether to replace the
CircuitStateFns with DictStateFns (from the counts) or VectorStateFns (from the
statevector). ``None`` will set this argument automatically based on the backend.
param_qobj: (TODO, not yet available) Whether to use Aer's parameterized Qobj
capability to avoid re-assembling the circuits.
attach_results: Whether to attach the data from the backend ``Results`` object for
a given ``CircuitStateFn``` to an ``execution_results`` field added the converted
``DictStateFn`` or ``VectorStateFn``.
Raises:
ValueError: Set statevector or param_qobj True when not supported by backend.
"""
self._quantum_instance = backend if isinstance(backend, QuantumInstance) else\
QuantumInstance(backend=backend)
self._statevector = statevector if statevector is not None \
else self.quantum_instance.is_statevector
self._param_qobj = param_qobj
self._attach_results = attach_results
self._check_quantum_instance_and_modes_consistent()
# Object state variables
self._last_op = None
self._reduced_op_cache = None
self._circuit_ops_cache = {}
self._transpiled_circ_cache = None
self._transpile_before_bind = True
self._binding_mappings = None
def _check_quantum_instance_and_modes_consistent(self) -> None:
""" Checks whether the statevector and param_qobj settings are compatible with the
backend
Raises:
ValueError: statevector or param_qobj are True when not supported by backend.
"""
if self._statevector and not is_statevector_backend(
self.quantum_instance.backend):
raise ValueError(
'Statevector mode for circuit sampling requires statevector '
'backend, not {}.'.format(self.quantum_instance.backend))
if self._param_qobj and not is_aer_provider(
self.quantum_instance.backend):
raise ValueError('Parameterized Qobj mode requires Aer '
'backend, not {}.'.format(
self.quantum_instance.backend))
@property
def backend(self) -> BaseBackend:
""" Returns the backend.
Returns:
The backend used by the CircuitSampler
"""
return self.quantum_instance.backend
@backend.setter
def backend(self, backend: BaseBackend):
""" Sets backend without additional configuration. """
self.set_backend(backend)
def set_backend(self, backend: BaseBackend, **kwargs) -> None:
""" Sets backend with configuration.
Raises:
ValueError: statevector or param_qobj are True when not supported by backend.
"""
self.quantum_instance = QuantumInstance(backend)
self.quantum_instance.set_config(**kwargs)
@property
def quantum_instance(self) -> QuantumInstance:
""" Returns the quantum instance.
Returns:
The QuantumInstance used by the CircuitSampler
"""
return self._quantum_instance
@quantum_instance.setter
def quantum_instance(
self, quantum_instance: Union[QuantumInstance,
BaseBackend]) -> None:
""" Sets the QuantumInstance.
Raises:
ValueError: statevector or param_qobj are True when not supported by backend.
"""
if isinstance(quantum_instance, BaseBackend):
quantum_instance = QuantumInstance(quantum_instance)
self._quantum_instance = quantum_instance
self._check_quantum_instance_and_modes_consistent()
# pylint: disable=arguments-differ
def convert(
self,
operator: OperatorBase,
params: Optional[Dict[Union[ParameterExpression, ParameterVector],
Union[float, List[float],
List[List[float]]]]] = None
) -> OperatorBase:
r"""
Converts the Operator to one in which the CircuitStateFns are replaced by
DictStateFns or VectorStateFns. Extracts the CircuitStateFns out of the Operator,
caches them, calls ``sample_circuits`` below to get their converted replacements,
and replaces the CircuitStateFns in operator with the replacement StateFns.
Args:
operator: The Operator to convert
params: A dictionary mapping parameters to either single binding values or lists of
binding values. The dictionary can also contain pairs of ParameterVectors with
lists of parameters or lists of lists of parameters to bind to them.
Returns:
The converted Operator with CircuitStateFns replaced by DictStateFns or VectorStateFns.
"""
if self._last_op is None or id(operator) != id(self._last_op):
# Clear caches
self._last_op = operator
self._reduced_op_cache = None
self._circuit_ops_cache = None
self._transpiled_circ_cache = None
self._transpile_before_bind = True
if not self._reduced_op_cache:
operator_dicts_replaced = operator.to_circuit_op()
self._reduced_op_cache = operator_dicts_replaced.reduce()
if not self._circuit_ops_cache:
self._circuit_ops_cache = {}
self._extract_circuitstatefns(self._reduced_op_cache)
if params:
num_parameterizations = len(list(params.values())[0])
param_bindings = [{
param: value_list[i]
for (param, value_list) in params.items()
} for i in range(num_parameterizations)]
else:
param_bindings = None
num_parameterizations = 1
# Don't pass circuits if we have in the cache, the sampling function knows to use the cache
circs = list(self._circuit_ops_cache.values()
) if not self._transpiled_circ_cache else None
sampled_statefn_dicts = self.sample_circuits(
circuit_sfns=circs, param_bindings=param_bindings)
def replace_circuits_with_dicts(operator, param_index=0):
if isinstance(operator, CircuitStateFn):
return sampled_statefn_dicts[id(operator)][param_index]
elif isinstance(operator, ListOp):
return operator.traverse(
partial(replace_circuits_with_dicts,
param_index=param_index))
else:
return operator
if params:
return ListOp([
replace_circuits_with_dicts(self._reduced_op_cache,
param_index=i)
for i in range(num_parameterizations)
])
else:
return replace_circuits_with_dicts(self._reduced_op_cache,
param_index=0)
def _extract_circuitstatefns(self, operator: OperatorBase) -> None:
r"""
Recursively extract the ``CircuitStateFns`` contained in operator into the
``_circuit_ops_cache`` field.
"""
if isinstance(operator, CircuitStateFn):
self._circuit_ops_cache[id(operator)] = operator
elif isinstance(operator, ListOp):
for op in operator.oplist:
self._extract_circuitstatefns(op)
def sample_circuits(
self,
circuit_sfns: Optional[List[CircuitStateFn]] = None,
param_bindings: Optional[List[Dict[ParameterExpression,
List[float]]]] = None
) -> Dict[int, Union[StateFn, List[StateFn]]]:
r"""
Samples the CircuitStateFns and returns a dict associating their ``id()`` values to their
replacement DictStateFn or VectorStateFn. If param_bindings is provided,
the CircuitStateFns are broken into their parameterizations, and a list of StateFns is
returned in the dict for each circuit ``id()``. Note that param_bindings is provided here
in a different format than in ``convert``, and lists of parameters within the dict is not
supported, and only binding dicts which are valid to be passed into Terra can be included
in this list.
Args:
circuit_sfns: The list of CircuitStateFns to sample.
param_bindings: The parameterizations to bind to each CircuitStateFn.
Returns:
The dictionary mapping ids of the CircuitStateFns to their replacement StateFns.
"""
if circuit_sfns or not self._transpiled_circ_cache:
if self._statevector:
circuits = [
op_c.to_circuit(meas=False) for op_c in circuit_sfns
]
else:
circuits = [
op_c.to_circuit(meas=True) for op_c in circuit_sfns
]
try:
self._transpiled_circ_cache = self.quantum_instance.transpile(
circuits)
except QiskitError:
logger.debug(
r'CircuitSampler failed to transpile circuits with unbound '
r'parameters. Attempting to transpile only when circuits are bound '
r'now, but this can hurt performance due to repeated transpilation.'
)
self._transpile_before_bind = False
self._transpiled_circ_cache = circuits
else:
circuit_sfns = list(self._circuit_ops_cache.values())
if param_bindings is not None:
if self._param_qobj:
ready_circs = self._transpiled_circ_cache
self._prepare_parameterized_run_config(param_bindings)
else:
ready_circs = [
circ.assign_parameters(binding)
for circ in self._transpiled_circ_cache
for binding in param_bindings
]
else:
ready_circs = self._transpiled_circ_cache
results = self.quantum_instance.execute(
ready_circs, had_transpiled=self._transpile_before_bind)
# Wipe parameterizations, if any
# self.quantum_instance._run_config.parameterizations = None
sampled_statefn_dicts = {}
for i, op_c in enumerate(circuit_sfns):
# Taking square root because we're replacing a statevector
# representation of probabilities.
reps = len(param_bindings) if param_bindings is not None else 1
c_statefns = []
for j in range(reps):
circ_index = (i * reps) + j
circ_results = results.data(circ_index)
if 'expval_measurement' in circ_results.get(
'snapshots', {}).get('expectation_value', {}):
snapshot_data = results.data(circ_index)['snapshots']
avg = snapshot_data['expectation_value'][
'expval_measurement'][0]['value']
if isinstance(avg, (list, tuple)):
# Aer versions before 0.4 use a list snapshot format
# which must be converted to a complex value.
avg = avg[0] + 1j * avg[1]
# Will be replaced with just avg when eval is called later
num_qubits = circuit_sfns[0].num_qubits
result_sfn = (Zero ^ num_qubits).adjoint() * avg
elif self._statevector:
result_sfn = StateFn(op_c.coeff *
results.get_statevector(circ_index))
else:
shots = self.quantum_instance._run_config.shots
result_sfn = StateFn({
b: (v * op_c.coeff / shots)**.5
for (b, v) in results.get_counts(circ_index).items()
})
if self._attach_results:
result_sfn.execution_results = circ_results
c_statefns.append(result_sfn)
sampled_statefn_dicts[id(op_c)] = c_statefns
return sampled_statefn_dicts
# TODO build Aer re-parameterized Qobj.
def _prepare_parameterized_run_config(self, param_bindings: dict) -> None:
raise NotImplementedError
def run_BO_vqe_parallel(
phys_params,
get_qubit_op,
ansatz,
info_sharing_mode='shared',
nb_iter=30,
nb_init='max',
init_jobs=1,
nb_shots=1024,
backend_name='qasm_simulator',
initial_layout=None,
optimization_level=3,
verbose=False,
dump_results=False,
):
"""
Run the BO VQE algorithm, parallelised over different physical parameter values. It
has different information sharing modes, documented below under `info_sharing_mode`
Parameters
----------
phys_params : array
The set of physical parameters to run the BO VQE for. In the chemistry case this
is the nuclear separations, for TFIM it is the set of B values
get_qubit_op : callable, (float) -> (WeightedPauliOperator,float)
Generates the qubit Hamiltonian as well as any energy shift that is missing from the
qubit Hamiltonian (could be added back as a identity Pauli string). The chemistry
functions above, e.g. `get_H2_qubit_op` would be an example
ansatz : ParameterisedAnsatz object
The variational ansatz object, an instance of a subclass of ParameterisedAnsatz
info_sharing_mode : {'independent','shared','random','left','right'}
(BO) This controls the evaluation sharing of the BO instances, cases:
'independent' : The BO do not share data, each only recieves its own evaluations
'shared' : Each BO obj gains access to evaluations of all of the others.
'random1' : The BO do not get the evaluations others have requested, but in
addition to their own they get an equivalent number of randomly chosen
parameter points
'random2' : The BO do not get the evaluations others have requested, but in
addition to their own they get an equivalent number of randomly chosen
parameter points. These points are not chosen fully at random, but instead
if x1 and x2 are BO[1] and BO[2]'s chosen evaluations respectively then BO[1]
get an additional point y2 that is |x2-x1| away from x1 but in a random
direction, similar for BO[2], etc.
'left', 'right' : Implement information sharing but in a directional way, so
that (using 'left' as an example) BO[1] gets its evaluation as well as
BO[0]; BO[2] gets its point as well as BO[1] and BO[0], etc. To ensure all
BO's get an equal number of evaluations this is padded with random points.
These points are not chosen fully at random, they are chosen in the same way
as 'random2' described above.
nb_iter : int, default 30
(BO) Sets the number of iteration rounds of the BO
nb_init : int or keyword 'max', default 'max'
(BO) Sets the number of initial data points to feed into the BO before starting
iteration rounds. If set to 'max' it will generate the maximum number of initial
points such that it submits `init_jobs` worth of circuits to a qiskit backend.
init_jobs : int, default 1
(BO) The number of qiskit jobs to use to generate initial data. (Most real device
backends accept up to 900 circuits in one job.)
nb_shots : int, default 1024
(Qiskit) Number of measurements shots when executing circuits
backend_name : name of qiskit backend, defaults to 'qasm_simulator'
(Qiskit) Refers to either a IBMQ provider or Aer simulator backend
initial_layout : int array, optional
(Qiskit) Passed to a `QuantumInstance` obj, used in transpiling
optimization_level : int, default 3
(Qiskit) Passed to a `QuantumInstance` obj, used in transpiling
verbose : bool, optional
Set level of output of the function
dump_results : bool, default False
Flag for whether or not to dump the accumulated results objs
Returns
-------
exact_energies : array, size=len(phys_params)
True energies of the molecular ground state at each distance
BO_energies : array, size=len(phys_params)
BO VQE estimatated energies of the molecular ground state at each distance
BO_energies_std : array, size=len(phys_params)
The std on the final estimate of BO_energies. This comes from the std of the
final circuit evaluation at the BO optimal, it does not include uncertainties
due to the BO's lack of confidence about its optimal
optimal_params : array, shape=(len(phys_params),nb_params)
Optimal parameter sets found for each separation
accumulated_results : list
If `dump_results` is set to True this will return the accumulated results dicts
from the BO, else it will be an empty list
"""
# to save results sets
accumulated_results = []
# check the information sharing arg is recognised
if not info_sharing_mode in [
'independent', 'shared', 'random1', 'random2', 'left', 'right'
]:
print(
'BO information sharing mode ' + f'{info_sharing_mode}' +
' not recognised, please choose: ' +
'"independent", "shared", "random1", "random2", "left" or "right".',
file=sys.stderr)
raise ValueError
# make qubit ops
if verbose: print('making qubit ops...')
qubit_ops, exact_energies, shifts = _make_qubit_ops(
phys_params, get_qubit_op)
# group pauli operators for measurement
qubit_ops = [
TPBGroupedWeightedPauliOperator.unsorted_grouping(op)
for op in qubit_ops
]
# ensure the ansatz and qubit Hamiltonians have same number of qubits
assert qubit_ops[0].num_qubits == ansatz.num_qubits
# create quantum instances
if (backend_name[:4] == 'ibmq'):
_is_device = True
from qiskit import IBMQ
IBMQ.load_account()
provider = IBMQ.get_provider(group='samsung')
backend = provider.get_backend(backend_name)
else:
_is_device = False
backend = Aer.get_backend(backend_name)
inst_fewshots = QuantumInstance(
backend,
shots=nb_shots,
optimization_level=optimization_level,
initial_layout=initial_layout,
skip_qobj_validation=(not _is_device),
)
inst_bigshots = QuantumInstance(
backend,
shots=8192,
optimization_level=optimization_level,
initial_layout=initial_layout,
skip_qobj_validation=(not _is_device),
)
# generate and transpile measurement circuits, using first of qubit_ops
measurement_circuits = qubit_ops[0].construct_evaluation_circuit(
wave_function=ansatz.circuit,
statevector_mode=inst_fewshots.is_statevector)
t_measurement_circuits = inst_fewshots.transpile(measurement_circuits)
def obtain_results(params_values,
quantum_instance,
circuit_name_prefixes=None):
"""
Function to wrap executions on the quantum backend, binds parameter values
and executes.
(Optionally `circuit_name_prefixes` arg sets the prefix names of the eval
circuits, else they are numbered sequentially. If the size of the name array
is different from the size of `params_values` it will crash.)
Gets `ansatz` and `t_measurement_circuits` from the surrounding scope.
`quantum_instance` is an arg because we switch between high and low shot
number instances.
"""
if np.ndim(params_values) == 1:
params_values = [params_values]
if circuit_name_prefixes is not None:
assert len(circuit_name_prefixes) == params_values.shape[0]
# package and bind circuits
bound_circs = []
for pidx, p in enumerate(params_values):
for cc in t_measurement_circuits:
tmp = cc.bind_parameters(dict(zip(ansatz.params, p)))
if circuit_name_prefixes is not None:
prefix = circuit_name_prefixes[pidx]
else:
prefix = str(pidx)
tmp.name = prefix + tmp.name
bound_circs.append(tmp)
# See if one can add a noise model here and the number of parameters
result = quantum_instance.execute(bound_circs, had_transpiled=True)
if dump_results:
accumulated_results.append(
[params_values.tolist(),
result.to_dict()])
return result
# ===================
# run BO Optim
# ===================
# setup
DOMAIN_FULL = [(0, 2 * np.pi) for i in range(ansatz.nb_params)]
DOMAIN_BO = [{
'name': str(i),
'type': 'continuous',
'domain': d
} for i, d in enumerate(DOMAIN_FULL)]
bo_args = ut.gen_default_argsbo()
bo_args.update({'domain': DOMAIN_BO, 'initial_design_numdata': 0})
# get initial values using bigshots
if nb_init == 'max':
_nb_init = init_jobs * 900 // len(t_measurement_circuits)
else:
_nb_init = nb_init
if verbose: print('getting initialisation data...')
Xinit = 2 * np.pi * np.random.random(_nb_init * ansatz.nb_params).reshape(
(_nb_init, ansatz.nb_params))
init_results = obtain_results(Xinit, inst_bigshots)
# initialise BO obj for each distance
Bopts = []
_update_weights = []
if verbose: print('initialising BO objects...')
for qo in qubit_ops:
# evaluate this distance's specific weighted qubit operators using the results
Yinit = np.array([[
np.real(
qo.evaluate_with_result(
init_results,
statevector_mode=inst_bigshots.is_statevector,
circuit_name_prefix=str(i))[0])
for i in range(Xinit.shape[0])
]]).T
tmp = GPyOpt.methods.BayesianOptimization(
lambda x: None, # blank cost function
X=Xinit,
Y=Yinit,
**bo_args)
tmp.run_optimization(max_iter=0, eps=0) # (may not be needed)
Bopts.append(tmp)
# This block is only to ensure the linear decrease of the exploration
if (getattr(tmp, '_dynamic_weights') == 'linear'):
_update_weights.append(True)
def dynamics_weight(n):
return max(0.000001,
bo_args['acquisition_weight'] * (1 - n / nb_iter))
else:
_update_weights.append(False)
# main loop
for iter_idx in range(nb_iter):
# for the last 5 shots use large number of measurements
inst = inst_fewshots
_msg = 'at optimisation round ' + f'{iter_idx}'
if nb_iter - iter_idx < 6:
inst = inst_bigshots
_msg += ' (using big shot number)'
if verbose: print(_msg)
# query each BO obj where it would like an evaluation, and pool them all togther
circuit_name_prefixes = []
circ_name_to_x_map = {}
for idx, bo in enumerate(Bopts):
bo._update_model(bo.normalization_type)
if (_update_weights[idx]):
bo.acquisition.exploration_weight = dynamics_weight(iter_idx)
x = bo._compute_next_evaluations()
if idx == 0:
Xnew = x
else:
Xnew = np.vstack((Xnew, x))
_circ_name = str(idx) + '-base'
circuit_name_prefixes.append(_circ_name)
circ_name_to_x_map[_circ_name] = x[0]
# to implement 'random1' we pad out each with completely random evaluations
if info_sharing_mode == 'random1':
nb_extra_points = len(phys_params) - 1
extra_points = 2 * np.pi * np.random.random(
nb_extra_points * ansatz.nb_params).reshape(
(nb_extra_points, ansatz.nb_params))
Xnew = np.vstack((Xnew, extra_points))
_circ_names = [
str(idx) + '-' + str(i) for i in range(len(phys_params))
if not i == idx
]
circuit_name_prefixes += _circ_names
circ_name_to_x_map.update(dict(zip(_circ_names, extra_points)))
# to implement 'random2','left','right' strategies we have to wait until all primary
# evaluations have been processed
tmp = copy.deepcopy(Xnew)
if info_sharing_mode in ['random2', 'left', 'right']:
for boidx, bo in enumerate(Bopts):
bo_eval_point = tmp[boidx]
for pidx, p in enumerate(tmp):
if (((info_sharing_mode == 'random2') and
(not boidx == pidx)) or
((info_sharing_mode == 'left') and (boidx < pidx)) or
((info_sharing_mode == 'right') and (boidx > pidx))):
# distance is euclidean distance, but since the values are angles we want
# to minimize the element-wise differences by optionally shifting one of
# the points by ±2\pi
disp_vector = np.minimum(
(bo_eval_point - p)**2,
((bo_eval_point + 2 * np.pi) - p)**2)
disp_vector = np.minimum(
disp_vector, ((bo_eval_point - 2 * np.pi) - p)**2)
dist = np.sqrt(np.sum(disp_vector))
# generate random vector in N-d space then scale it to have length we want,
# using 'Hypersphere Point Picking' Gaussian approach
random_displacement = np.random.normal(
size=ansatz.nb_params)
random_displacement = random_displacement * dist / np.sqrt(
np.sum(random_displacement**2))
Xnew = np.vstack(
(Xnew, bo_eval_point + random_displacement))
_circ_name = str(boidx) + '-' + str(pidx)
circuit_name_prefixes.append(_circ_name)
circ_name_to_x_map[
_circ_name] = bo_eval_point + random_displacement
# sense check on number of circuits generated
if info_sharing_mode in ['independent', 'shared']:
assert len(circuit_name_prefixes) == len(phys_params)
elif info_sharing_mode in ['random1', 'random2']:
assert len(circuit_name_prefixes) == len(phys_params)**2
elif info_sharing_mode in ['left', 'right']:
assert len(circuit_name_prefixes) == len(phys_params) * (
len(phys_params) + 1) // 2
# get results object at new param values
new_results = obtain_results(
Xnew, inst, circuit_name_prefixes=circuit_name_prefixes)
# iterate over the BO obj's passing them all the correctly weighted data
for idx, (bo, qo) in enumerate(zip(Bopts, qubit_ops)):
if info_sharing_mode == 'independent':
_pull_from = [str(idx) + '-base']
elif info_sharing_mode == 'shared':
_pull_from = [
str(i) + '-base' for i in range(len(phys_params))
]
elif info_sharing_mode in ['random1', 'random2']:
_pull_from = ([str(idx) + '-base'] + [
str(idx) + '-' + str(i)
for i in range(len(phys_params)) if not i == idx
])
elif info_sharing_mode == 'left':
_pull_from = ([str(i) + '-base' for i in range(idx + 1)] + [
str(idx) + '-' + str(i)
for i in range(idx + 1, len(phys_params))
])
elif info_sharing_mode == 'right':
_pull_from = (
[str(i) + '-base' for i in range(idx, len(phys_params))] +
[str(idx) + '-' + str(i) for i in range(idx)])
if not info_sharing_mode == 'independent':
assert len(_pull_from) == len(phys_params) # sense check
Ynew = np.array([[
np.real(
qo.evaluate_with_result(
new_results,
statevector_mode=inst_bigshots.is_statevector,
circuit_name_prefix=i)[0]) for i in _pull_from
]]).T
Xnew = np.array([circ_name_to_x_map[i] for i in _pull_from])
bo.X = np.vstack((bo.X, Xnew))
bo.Y = np.vstack((bo.Y, Ynew))
#finalize (may not be needed)
if verbose: print('finalising...')
for bo in Bopts:
bo.run_optimization(max_iter=0, eps=0)
# Results found
for idx, (dist, bo) in enumerate(zip(phys_params, Bopts)):
(Xseen, Yseen), (Xexp, Yexp) = bo.get_best()
if verbose:
print("at distance " + f'{dist}')
print("best seen: " + f'{Yseen}')
print("best expected: " + f'{Yexp}')
print(bo.model.model)
bo.plot_convergence()
if idx == 0:
Xfinal = Xexp
else:
Xfinal = np.vstack([Xfinal, Xexp])
# obtain final BO estimates from an evaluation of the optimal params
final_results = obtain_results(Xfinal, inst_bigshots)
BO_energies = np.zeros(len(phys_params))
BO_energies_std = np.zeros(len(phys_params))
for idx, qo in enumerate(qubit_ops):
mean, std = qo.evaluate_with_result(
final_results,
statevector_mode=inst_bigshots.is_statevector,
circuit_name_prefix=str(idx))
BO_energies[idx] = np.real(mean)
BO_energies_std[idx] = np.real(std)
return exact_energies + shifts, BO_energies + shifts, BO_energies_std, Xfinal, accumulated_results
class CircuitSampler(ConverterBase):
"""
The CircuitSampler traverses an Operator and converts any CircuitStateFns into
approximations of the state function by a DictStateFn or VectorStateFn using a quantum
backend. Note that in order to approximate the value of the CircuitStateFn, it must 1) send
state function through a depolarizing channel, which will destroy all phase information and
2) replace the sampled frequencies with **square roots** of the frequency, rather than the raw
probability of sampling (which would be the equivalent of sampling the **square** of the
state function, per the Born rule.
The CircuitSampler aggressively caches transpiled circuits to handle re-parameterization of
the same circuit efficiently. If you are converting multiple different Operators,
you are better off using a different CircuitSampler for each Operator to avoid cache thrashing.
"""
def __init__(self,
backend: Union[Backend, BaseBackend, QuantumInstance],
statevector: Optional[bool] = None,
param_qobj: bool = False,
attach_results: bool = False) -> None:
"""
Args:
backend: The quantum backend or QuantumInstance to use to sample the circuits.
statevector: If backend is a statevector backend, whether to replace the
CircuitStateFns with DictStateFns (from the counts) or VectorStateFns (from the
statevector). ``None`` will set this argument automatically based on the backend.
attach_results: Whether to attach the data from the backend ``Results`` object for
a given ``CircuitStateFn``` to an ``execution_results`` field added the converted
``DictStateFn`` or ``VectorStateFn``.
param_qobj: Whether to use Aer's parameterized Qobj capability to avoid re-assembling
the circuits.
Raises:
ValueError: Set statevector or param_qobj True when not supported by backend.
"""
self._quantum_instance = backend if isinstance(backend, QuantumInstance) else\
QuantumInstance(backend=backend)
self._statevector = statevector if statevector is not None \
else self.quantum_instance.is_statevector
self._param_qobj = param_qobj
self._attach_results = attach_results
self._check_quantum_instance_and_modes_consistent()
# Object state variables
self._last_op = None
self._reduced_op_cache = None
self._circuit_ops_cache = {} # type: Dict[int, CircuitStateFn]
self._transpiled_circ_cache = None # type: Optional[List[Any]]
self._transpiled_circ_templates = None # type: Optional[List[Any]]
self._transpile_before_bind = True
self._binding_mappings = None
def _check_quantum_instance_and_modes_consistent(self) -> None:
""" Checks whether the statevector and param_qobj settings are compatible with the
backend
Raises:
ValueError: statevector or param_qobj are True when not supported by backend.
"""
if self._statevector and not is_statevector_backend(
self.quantum_instance.backend):
raise ValueError(
'Statevector mode for circuit sampling requires statevector '
'backend, not {}.'.format(self.quantum_instance.backend))
if self._param_qobj and not is_aer_provider(
self.quantum_instance.backend):
raise ValueError('Parameterized Qobj mode requires Aer '
'backend, not {}.'.format(
self.quantum_instance.backend))
@property
def backend(self) -> Union[Backend, BaseBackend]:
""" Returns the backend.
Returns:
The backend used by the CircuitSampler
"""
return self.quantum_instance.backend
@backend.setter
def backend(self, backend: Union[Backend, BaseBackend]):
""" Sets backend without additional configuration. """
self.set_backend(backend)
def set_backend(self, backend: Union[Backend, BaseBackend],
**kwargs) -> None:
""" Sets backend with configuration.
Raises:
ValueError: statevector or param_qobj are True when not supported by backend.
"""
self.quantum_instance = QuantumInstance(backend)
self.quantum_instance.set_config(**kwargs)
@property
def quantum_instance(self) -> QuantumInstance:
""" Returns the quantum instance.
Returns:
The QuantumInstance used by the CircuitSampler
"""
return self._quantum_instance
@quantum_instance.setter
def quantum_instance(
self, quantum_instance: Union[QuantumInstance, Backend,
BaseBackend]) -> None:
""" Sets the QuantumInstance.
Raises:
ValueError: statevector or param_qobj are True when not supported by backend.
"""
if isinstance(quantum_instance, (Backend, BaseBackend)):
quantum_instance = QuantumInstance(quantum_instance)
self._quantum_instance = quantum_instance
self._check_quantum_instance_and_modes_consistent()
# pylint: disable=arguments-differ
def convert(
self,
operator: OperatorBase,
params: Optional[Dict[Parameter, Union[float, List[float],
List[List[float]]]]] = None
) -> OperatorBase:
r"""
Converts the Operator to one in which the CircuitStateFns are replaced by
DictStateFns or VectorStateFns. Extracts the CircuitStateFns out of the Operator,
caches them, calls ``sample_circuits`` below to get their converted replacements,
and replaces the CircuitStateFns in operator with the replacement StateFns.
Args:
operator: The Operator to convert
params: A dictionary mapping parameters to either single binding values or lists of
binding values.
Returns:
The converted Operator with CircuitStateFns replaced by DictStateFns or VectorStateFns.
Raises:
AquaError: if extracted circuits are empty.
"""
if self._last_op is None or id(operator) != id(self._last_op):
# Clear caches
self._last_op = operator
self._reduced_op_cache = None
self._circuit_ops_cache = None
self._transpiled_circ_cache = None
self._transpile_before_bind = True
if not self._reduced_op_cache:
operator_dicts_replaced = operator.to_circuit_op()
self._reduced_op_cache = operator_dicts_replaced.reduce()
if not self._circuit_ops_cache:
self._circuit_ops_cache = {}
self._extract_circuitstatefns(self._reduced_op_cache)
if not self._circuit_ops_cache:
raise AquaError(
'Circuits are empty. '
'Check that the operator is an instance of CircuitStateFn or its ListOp.'
)
if params is not None and len(params.keys()) > 0:
p_0 = list(params.values())[0] # type: ignore
if isinstance(p_0, (list, np.ndarray)):
num_parameterizations = len(cast(List, p_0))
param_bindings = [
{
param: value_list[i] # type: ignore
for (param, value_list) in params.items()
} for i in range(num_parameterizations)
]
else:
num_parameterizations = 1
param_bindings = [params] # type: ignore
else:
param_bindings = None
num_parameterizations = 1
# Don't pass circuits if we have in the cache, the sampling function knows to use the cache
circs = list(self._circuit_ops_cache.values()
) if not self._transpiled_circ_cache else None
p_b = cast(List[Dict[Parameter, float]], param_bindings)
sampled_statefn_dicts = self.sample_circuits(circuit_sfns=circs,
param_bindings=p_b)
def replace_circuits_with_dicts(operator, param_index=0):
if isinstance(operator, CircuitStateFn):
return sampled_statefn_dicts[id(operator)][param_index]
elif isinstance(operator, ListOp):
return operator.traverse(
partial(replace_circuits_with_dicts,
param_index=param_index))
else:
return operator
if params:
return ListOp([
replace_circuits_with_dicts(self._reduced_op_cache,
param_index=i)
for i in range(num_parameterizations)
])
else:
return replace_circuits_with_dicts(self._reduced_op_cache,
param_index=0)
def _extract_circuitstatefns(self, operator: OperatorBase) -> None:
r"""
Recursively extract the ``CircuitStateFns`` contained in operator into the
``_circuit_ops_cache`` field.
"""
if isinstance(operator, CircuitStateFn):
self._circuit_ops_cache[id(operator)] = operator
elif isinstance(operator, ListOp):
for op in operator.oplist:
self._extract_circuitstatefns(op)
def sample_circuits(
self,
circuit_sfns: Optional[List[CircuitStateFn]] = None,
param_bindings: Optional[List[Dict[Parameter, float]]] = None
) -> Dict[int, Union[StateFn, List[StateFn]]]:
r"""
Samples the CircuitStateFns and returns a dict associating their ``id()`` values to their
replacement DictStateFn or VectorStateFn. If param_bindings is provided,
the CircuitStateFns are broken into their parameterizations, and a list of StateFns is
returned in the dict for each circuit ``id()``. Note that param_bindings is provided here
in a different format than in ``convert``, and lists of parameters within the dict is not
supported, and only binding dicts which are valid to be passed into Terra can be included
in this list.
Args:
circuit_sfns: The list of CircuitStateFns to sample.
param_bindings: The parameterizations to bind to each CircuitStateFn.
Returns:
The dictionary mapping ids of the CircuitStateFns to their replacement StateFns.
Raises:
AquaError: if extracted circuits are empty.
"""
if not circuit_sfns and not self._transpiled_circ_cache:
raise AquaError('CircuitStateFn is empty and there is no cache.')
if circuit_sfns:
self._transpiled_circ_templates = None
if self._statevector:
circuits = [
op_c.to_circuit(meas=False) for op_c in circuit_sfns
]
else:
circuits = [
op_c.to_circuit(meas=True) for op_c in circuit_sfns
]
try:
self._transpiled_circ_cache = self.quantum_instance.transpile(
circuits)
except QiskitError:
logger.debug(
r'CircuitSampler failed to transpile circuits with unbound '
r'parameters. Attempting to transpile only when circuits are bound '
r'now, but this can hurt performance due to repeated transpilation.'
)
self._transpile_before_bind = False
self._transpiled_circ_cache = circuits
else:
circuit_sfns = list(self._circuit_ops_cache.values())
if param_bindings is not None:
if self._param_qobj:
start_time = time()
ready_circs = self._prepare_parameterized_run_config(
param_bindings)
end_time = time()
logger.debug('Parameter conversion %.5f (ms)',
(end_time - start_time) * 1000)
else:
start_time = time()
ready_circs = [
circ.assign_parameters(_filter_params(circ, binding))
for circ in self._transpiled_circ_cache
for binding in param_bindings
]
end_time = time()
logger.debug('Parameter binding %.5f (ms)',
(end_time - start_time) * 1000)
else:
ready_circs = self._transpiled_circ_cache
results = self.quantum_instance.execute(
ready_circs, had_transpiled=self._transpile_before_bind)
if param_bindings is not None and self._param_qobj:
self._clean_parameterized_run_config()
# Wipe parameterizations, if any
# self.quantum_instance._run_config.parameterizations = None
sampled_statefn_dicts = {}
for i, op_c in enumerate(circuit_sfns):
# Taking square root because we're replacing a statevector
# representation of probabilities.
reps = len(param_bindings) if param_bindings is not None else 1
c_statefns = []
for j in range(reps):
circ_index = (i * reps) + j
circ_results = results.data(circ_index)
if 'expval_measurement' in circ_results.get(
'snapshots', {}).get('expectation_value', {}):
snapshot_data = results.data(circ_index)['snapshots']
avg = snapshot_data['expectation_value'][
'expval_measurement'][0]['value']
if isinstance(avg, (list, tuple)):
# Aer versions before 0.4 use a list snapshot format
# which must be converted to a complex value.
avg = avg[0] + 1j * avg[1]
# Will be replaced with just avg when eval is called later
num_qubits = circuit_sfns[0].num_qubits
result_sfn = DictStateFn(
'0' * num_qubits,
is_measurement=op_c.is_measurement) * avg
elif self._statevector:
result_sfn = StateFn(op_c.coeff *
results.get_statevector(circ_index),
is_measurement=op_c.is_measurement)
else:
shots = self.quantum_instance._run_config.shots
result_sfn = StateFn(
{
b: (v / shots)**0.5 * op_c.coeff
for (b,
v) in results.get_counts(circ_index).items()
},
is_measurement=op_c.is_measurement)
if self._attach_results:
result_sfn.execution_results = circ_results
c_statefns.append(result_sfn)
sampled_statefn_dicts[id(op_c)] = c_statefns
return sampled_statefn_dicts
def _build_aer_params(self, circuit: QuantumCircuit,
building_param_tables: Dict[Tuple[int, int],
List[float]],
input_params: Dict[Parameter, float]) -> None:
def resolve_param(inst_param):
if not isinstance(inst_param, ParameterExpression):
return None
param_mappings = {}
for param in inst_param._parameter_symbols.keys():
if param not in input_params:
raise ValueError('unexpected parameter: {0}'.format(param))
param_mappings[param] = input_params[param]
return float(inst_param.bind(param_mappings))
gate_index = 0
for inst, _, _ in circuit.data:
param_index = 0
for inst_param in inst.params:
val = resolve_param(inst_param)
if val is not None:
param_key = (gate_index, param_index)
if param_key in building_param_tables:
building_param_tables[param_key].append(val)
else:
building_param_tables[param_key] = [val]
param_index += 1
gate_index += 1
def _prepare_parameterized_run_config(
self, param_bindings: List[Dict[Parameter, float]]) -> List[Any]:
self.quantum_instance._run_config.parameterizations = []
if self._transpiled_circ_templates is None \
or len(self._transpiled_circ_templates) != len(self._transpiled_circ_cache):
# temporally resolve parameters of self._transpiled_circ_cache
# They will be overridden in Aer from the next iterations
self._transpiled_circ_templates = [
circ.assign_parameters(_filter_params(circ, param_bindings[0]))
for circ in self._transpiled_circ_cache
]
for circ in self._transpiled_circ_cache:
building_param_tables = {
} # type: Dict[Tuple[int, int], List[float]]
for param_binding in param_bindings:
self._build_aer_params(circ, building_param_tables,
param_binding)
param_tables = []
for gate_and_param_indices in building_param_tables:
gate_index = gate_and_param_indices[0]
param_index = gate_and_param_indices[1]
param_tables.append([[gate_index, param_index],
building_param_tables[(gate_index,
param_index)]])
self.quantum_instance._run_config.parameterizations.append(
param_tables)
return self._transpiled_circ_templates
def _clean_parameterized_run_config(self) -> None:
self.quantum_instance._run_config.parameterizations = []
