def solve(
self,
problem: BaseProblem,
aux_operators: Optional[ListOrDictType[Union[SecondQuantizedOp,
PauliSumOp]]] = None,
) -> EigenstateResult:
"""Compute Ground State properties.
Args:
problem: a class encoding a problem to be solved.
aux_operators: Additional auxiliary operators to evaluate.
Raises:
ValueError: if the grouped property object returned by the driver does not contain a
main property as requested by the problem being solved (`problem.main_property_name`)
QiskitNatureError: if the user-provided `aux_operators` contain a name which clashes
with an internally constructed auxiliary operator. Note: the names used for the
internal auxiliary operators correspond to the `Property.name` attributes which
generated the respective operators.
Returns:
An interpreted :class:`~.EigenstateResult`. For more information see also
:meth:`~.BaseProblem.interpret`.
"""
main_operator, aux_ops = self.get_qubit_operators(
problem, aux_operators)
raw_mes_result = self._solver.compute_minimum_eigenvalue(
main_operator, aux_ops) # type: ignore
result = problem.interpret(raw_mes_result)
return result
def solve(
self,
problem: BaseProblem,
aux_operators: Optional[List[Union[SecondQuantizedOp,
PauliSumOp]]] = None,
) -> EigenstateResult:
"""Compute Ground and Excited States properties.
Args:
problem: a class encoding a problem to be solved.
aux_operators: Additional auxiliary operators to evaluate.
Raises:
NotImplementedError: If an operator in ``aux_operators`` is not of type
``FermionicOperator``.
Returns:
An interpreted :class:`~.EigenstateResult`. For more information see also
:meth:`~.BaseProblem.interpret`.
"""
# get the operator and auxiliary operators, and transform the provided auxiliary operators
# note that ``aux_operators`` contains not only the transformed ``aux_operators`` passed
# by the user but also additional ones from the transformation
second_q_ops = problem.second_q_ops()
main_operator = self._qubit_converter.convert(
second_q_ops[0],
num_particles=problem.num_particles,
sector_locator=problem.symmetry_sector_locator,
)
aux_ops = self._qubit_converter.convert_match(second_q_ops[1:])
if aux_operators is not None:
for aux_op in aux_operators:
if isinstance(aux_op, SecondQuantizedOp):
aux_ops.append(
self._qubit_converter.convert_match(aux_op, True))
else:
aux_ops.append(aux_op)
if isinstance(self._solver, EigensolverFactory):
# this must be called after transformation.transform
solver = self._solver.get_solver(problem)
else:
solver = self._solver
# if the eigensolver does not support auxiliary operators, reset them
if not solver.supports_aux_operators():
aux_ops = None
raw_es_result = solver.compute_eigenvalues(main_operator, aux_ops)
eigenstate_result = EigenstateResult()
eigenstate_result.raw_result = raw_es_result
eigenstate_result.eigenenergies = raw_es_result.eigenvalues
eigenstate_result.eigenstates = raw_es_result.eigenstates
eigenstate_result.aux_operator_eigenvalues = raw_es_result.aux_operator_eigenvalues
result = problem.interpret(eigenstate_result)
return result
def solve(
self,
problem: BaseProblem,
aux_operators: Optional[ListOrDictType[Union[SecondQuantizedOp,
PauliSumOp]]] = None,
) -> EigenstateResult:
"""Compute Ground State properties.
Args:
problem: a class encoding a problem to be solved.
aux_operators: Additional auxiliary operators to evaluate.
Raises:
ValueError: if the grouped property object returned by the driver does not contain a
main property as requested by the problem being solved (`problem.main_property_name`)
QiskitNatureError: if the user-provided `aux_operators` contain a name which clashes
with an internally constructed auxiliary operator. Note: the names used for the
internal auxiliary operators correspond to the `Property.name` attributes which
generated the respective operators.
Returns:
An interpreted :class:`~.EigenstateResult`. For more information see also
:meth:`~.BaseProblem.interpret`.
"""
# get the operator and auxiliary operators, and transform the provided auxiliary operators
# note that ``aux_ops`` contains not only the transformed ``aux_operators`` passed by the
# user but also additional ones from the transformation
second_q_ops = problem.second_q_ops()
aux_second_q_ops: ListOrDictType[SecondQuantizedOp]
if isinstance(second_q_ops, list):
main_second_q_op = second_q_ops[0]
aux_second_q_ops = second_q_ops[1:]
elif isinstance(second_q_ops, dict):
name = problem.main_property_name
main_second_q_op = second_q_ops.pop(name, None)
if main_second_q_op is None:
raise ValueError(
f"The main `SecondQuantizedOp` associated with the {name} property cannot be "
"`None`.")
aux_second_q_ops = second_q_ops
main_operator = self._qubit_converter.convert(
main_second_q_op,
num_particles=problem.num_particles,
sector_locator=problem.symmetry_sector_locator,
)
aux_ops = self._qubit_converter.convert_match(aux_second_q_ops)
if aux_operators is not None:
wrapped_aux_operators: ListOrDict[Union[
SecondQuantizedOp, PauliSumOp]] = ListOrDict(aux_operators)
for name, aux_op in iter(wrapped_aux_operators):
if isinstance(aux_op, SecondQuantizedOp):
converted_aux_op = self._qubit_converter.convert_match(
aux_op, True)
else:
converted_aux_op = aux_op
if isinstance(aux_ops, list):
aux_ops.append(converted_aux_op)
elif isinstance(aux_ops, dict):
if name in aux_ops.keys():
raise QiskitNatureError(
f"The key '{name}' is already taken by an internally constructed "
"auxliliary operator! Please use a different name for your custom "
"operator.")
aux_ops[name] = converted_aux_op
if isinstance(self._solver, MinimumEigensolverFactory):
# this must be called after transformation.transform
self._solver = self._solver.get_solver(problem,
self._qubit_converter)
# if the eigensolver does not support auxiliary operators, reset them
if not self._solver.supports_aux_operators():
aux_ops = None
raw_mes_result = self._solver.compute_minimum_eigenvalue(
main_operator, aux_ops)
result = problem.interpret(raw_mes_result)
return result
def solve(
self,
problem: BaseProblem,
aux_operators: Optional[List[SecondQuantizedOp]] = None
) -> EigenstateResult:
"""Run the excited-states calculation.
Construct and solves the EOM pseudo-eigenvalue problem to obtain the excitation energies
and the excitation operators expansion coefficients.
Args:
problem: a class encoding a problem to be solved.
aux_operators: Additional auxiliary operators to evaluate.
Returns:
An interpreted :class:`~.EigenstateResult`. For more information see also
:meth:`~.BaseProblem.interpret`.
"""
if aux_operators is not None:
logger.warning(
"With qEOM the auxiliary operators can currently only be "
"evaluated on the ground state.")
# 1. Run ground state calculation
groundstate_result = self._gsc.solve(problem)
# 2. Prepare the excitation operators
self._untapered_qubit_op_main = self._gsc._qubit_converter.map(
problem.second_q_ops()[0])
matrix_operators_dict, size = self._prepare_matrix_operators(problem)
# 3. Evaluate eom operators
measurement_results = self._gsc.evaluate_operators(
groundstate_result.eigenstates[0], matrix_operators_dict)
measurement_results = cast(Dict[str, List[float]], measurement_results)
# 4. Post-process ground_state_result to construct eom matrices
m_mat, v_mat, q_mat, w_mat, m_mat_std, v_mat_std, q_mat_std, w_mat_std = \
self._build_eom_matrices(measurement_results, size)
# 5. solve pseudo-eigenvalue problem
energy_gaps, expansion_coefs = self._compute_excitation_energies(
m_mat, v_mat, q_mat, w_mat)
qeom_result = QEOMResult()
qeom_result.ground_state_raw_result = groundstate_result.raw_result
qeom_result.expansion_coefficients = expansion_coefs
qeom_result.excitation_energies = energy_gaps
qeom_result.m_matrix = m_mat
qeom_result.v_matrix = v_mat
qeom_result.q_matrix = q_mat
qeom_result.w_matrix = w_mat
qeom_result.m_matrix_std = m_mat_std
qeom_result.v_matrix_std = v_mat_std
qeom_result.q_matrix_std = q_mat_std
qeom_result.w_matrix_std = w_mat_std
eigenstate_result = EigenstateResult()
eigenstate_result.eigenstates = groundstate_result.eigenstates
eigenstate_result.aux_operator_eigenvalues = groundstate_result.aux_operator_eigenvalues
eigenstate_result.raw_result = qeom_result
eigenstate_result.eigenenergies = np.append(
groundstate_result.eigenenergies,
np.asarray([
groundstate_result.eigenenergies[0] + gap
for gap in energy_gaps
]))
result = problem.interpret(eigenstate_result)
return result
def solve(
self,
problem: BaseProblem,
aux_operators: Optional[List[Union[SecondQuantizedOp,
PauliSumOp]]] = None,
) -> "AdaptVQEResult":
"""Computes the ground state.
Args:
problem: a class encoding a problem to be solved.
aux_operators: Additional auxiliary operators to evaluate.
Raises:
QiskitNatureError: if a solver other than VQE or a ansatz other than UCCSD is provided
or if the algorithm finishes due to an unforeseen reason.
Returns:
An AdaptVQEResult which is an ElectronicStructureResult but also includes runtime
information about the AdaptVQE algorithm like the number of iterations, finishing
criterion, and the final maximum gradient.
"""
second_q_ops = problem.second_q_ops()
self._main_operator = self._qubit_converter.convert(
second_q_ops[0],
num_particles=problem.num_particles,
sector_locator=problem.symmetry_sector_locator,
)
aux_ops = self._qubit_converter.convert_match(second_q_ops[1:])
if aux_operators is not None:
for aux_op in aux_operators:
if isinstance(aux_op, SecondQuantizedOp):
aux_ops.append(
self._qubit_converter.convert_match(aux_op, True))
else:
aux_ops.append(aux_op)
if isinstance(self._solver, MinimumEigensolverFactory):
vqe = self._solver.get_solver(problem, self._qubit_converter)
else:
vqe = self._solver
if not isinstance(vqe, VQE):
raise QiskitNatureError(
"The AdaptVQE algorithm requires the use of the VQE solver")
if not isinstance(vqe.ansatz, UCC):
raise QiskitNatureError(
"The AdaptVQE algorithm requires the use of the UCC ansatz")
# We construct the ansatz once to be able to extract the full set of excitation operators.
self._ansatz = copy.deepcopy(vqe.ansatz)
self._ansatz._build()
self._excitation_pool = copy.deepcopy(self._ansatz.operators)
threshold_satisfied = False
alternating_sequence = False
max_iterations_exceeded = False
prev_op_indices: List[int] = []
theta: List[float] = []
max_grad: Tuple[float, Optional[PauliSumOp]] = (0.0, None)
iteration = 0
while self._max_iterations is None or iteration < self._max_iterations:
iteration += 1
logger.info("--- Iteration #%s ---", str(iteration))
# compute gradients
cur_grads = self._compute_gradients(theta, vqe)
# pick maximum gradient
max_grad_index, max_grad = max(enumerate(cur_grads),
key=lambda item: np.abs(item[1][0]))
# store maximum gradient's index for cycle detection
prev_op_indices.append(max_grad_index)
# log gradients
if logger.isEnabledFor(logging.INFO):
gradlog = "\nGradients in iteration #{}".format(str(iteration))
gradlog += "\nID: Excitation Operator: Gradient <(*) maximum>"
for i, grad in enumerate(cur_grads):
gradlog += "\n{}: {}: {}".format(str(i), str(grad[1]),
str(grad[0]))
if grad[1] == max_grad[1]:
gradlog += "\t(*)"
logger.info(gradlog)
if np.abs(max_grad[0]) < self._threshold:
logger.info(
"Adaptive VQE terminated successfully with a final maximum gradient: %s",
str(np.abs(max_grad[0])),
)
threshold_satisfied = True
break
# check indices of picked gradients for cycles
if self._check_cyclicity(prev_op_indices):
logger.info("Alternating sequence found. Finishing.")
logger.info("Final maximum gradient: %s",
str(np.abs(max_grad[0])))
alternating_sequence = True
break
# add new excitation to self._ansatz
self._excitation_list.append(max_grad[1])
theta.append(0.0)
# run VQE on current Ansatz
self._ansatz.operators = self._excitation_list
vqe.ansatz = self._ansatz
vqe.initial_point = theta
raw_vqe_result = vqe.compute_minimum_eigenvalue(
self._main_operator)
theta = raw_vqe_result.optimal_point.tolist()
else:
# reached maximum number of iterations
max_iterations_exceeded = True
logger.info("Maximum number of iterations reached. Finishing.")
logger.info("Final maximum gradient: %s", str(np.abs(max_grad[0])))
# once finished evaluate auxiliary operators if any
if aux_ops is not None:
aux_values = self.evaluate_operators(raw_vqe_result.eigenstate,
aux_ops)
else:
aux_values = None
raw_vqe_result.aux_operator_eigenvalues = aux_values
if threshold_satisfied:
finishing_criterion = "Threshold converged"
elif alternating_sequence:
finishing_criterion = "Aborted due to cyclicity"
elif max_iterations_exceeded:
finishing_criterion = "Maximum number of iterations reached"
else:
raise QiskitNatureError(
"The algorithm finished due to an unforeseen reason!")
electronic_result = problem.interpret(raw_vqe_result)
result = AdaptVQEResult()
result.combine(electronic_result)
result.num_iterations = iteration
result.final_max_gradient = max_grad[0]
result.finishing_criterion = finishing_criterion
logger.info("The final energy is: %s",
str(result.computed_energies[0]))
return result
def solve(
self,
problem: BaseProblem,
aux_operators: Optional[ListOrDictType[Union[SecondQuantizedOp, PauliSumOp]]] = None,
) -> "AdaptVQEResult":
"""Computes the ground state.
Args:
problem: a class encoding a problem to be solved.
aux_operators: Additional auxiliary operators to evaluate.
Raises:
QiskitNatureError: if a solver other than VQE or a ansatz other than UCCSD is provided
or if the algorithm finishes due to an unforeseen reason.
ValueError: if the grouped property object returned by the driver does not contain a
main property as requested by the problem being solved (`problem.main_property_name`)
QiskitNatureError: if the user-provided `aux_operators` contain a name which clashes
with an internally constructed auxiliary operator. Note: the names used for the
internal auxiliary operators correspond to the `Property.name` attributes which
generated the respective operators.
Returns:
An AdaptVQEResult which is an ElectronicStructureResult but also includes runtime
information about the AdaptVQE algorithm like the number of iterations, finishing
criterion, and the final maximum gradient.
"""
second_q_ops = problem.second_q_ops()
aux_second_q_ops: ListOrDictType[SecondQuantizedOp]
if isinstance(second_q_ops, list):
main_second_q_op = second_q_ops[0]
aux_second_q_ops = second_q_ops[1:]
elif isinstance(second_q_ops, dict):
name = problem.main_property_name
main_second_q_op = second_q_ops.pop(name, None)
if main_second_q_op is None:
raise ValueError(
f"The main `SecondQuantizedOp` associated with the {name} property cannot be "
"`None`."
)
aux_second_q_ops = second_q_ops
self._main_operator = self._qubit_converter.convert(
main_second_q_op,
num_particles=problem.num_particles,
sector_locator=problem.symmetry_sector_locator,
)
aux_ops = self._qubit_converter.convert_match(aux_second_q_ops)
if aux_operators is not None:
wrapped_aux_operators: ListOrDict[Union[SecondQuantizedOp, PauliSumOp]] = ListOrDict(
aux_operators
)
for name_aux, aux_op in iter(wrapped_aux_operators):
if isinstance(aux_op, SecondQuantizedOp):
converted_aux_op = self._qubit_converter.convert_match(aux_op, True)
else:
converted_aux_op = aux_op
if isinstance(aux_ops, list):
aux_ops.append(converted_aux_op)
elif isinstance(aux_ops, dict):
if name_aux in aux_ops.keys():
raise QiskitNatureError(
f"The key '{name_aux}' is already taken by an internally constructed "
"auxiliary operator! Please use a different name for your custom "
"operator."
)
aux_ops[name_aux] = converted_aux_op
if isinstance(self._solver, MinimumEigensolverFactory):
vqe = self._solver.get_solver(problem, self._qubit_converter)
else:
vqe = self._solver
if not isinstance(vqe, VQE):
raise QiskitNatureError("The AdaptVQE algorithm requires the use of the VQE solver")
if not isinstance(vqe.ansatz, UCC):
raise QiskitNatureError("The AdaptVQE algorithm requires the use of the UCC ansatz")
# We construct the ansatz once to be able to extract the full set of excitation operators.
self._ansatz = copy.deepcopy(vqe.ansatz)
self._ansatz._build()
self._excitation_pool = copy.deepcopy(self._ansatz.operators)
threshold_satisfied = False
alternating_sequence = False
max_iterations_exceeded = False
prev_op_indices: List[int] = []
theta: List[float] = []
max_grad: Tuple[float, Optional[PauliSumOp]] = (0.0, None)
iteration = 0
while self._max_iterations is None or iteration < self._max_iterations:
iteration += 1
logger.info("--- Iteration #%s ---", str(iteration))
# compute gradients
cur_grads = self._compute_gradients(theta, vqe)
# pick maximum gradient
max_grad_index, max_grad = max(
enumerate(cur_grads), key=lambda item: np.abs(item[1][0])
)
# store maximum gradient's index for cycle detection
prev_op_indices.append(max_grad_index)
# log gradients
if logger.isEnabledFor(logging.INFO):
gradlog = f"\nGradients in iteration #{str(iteration)}"
gradlog += "\nID: Excitation Operator: Gradient <(*) maximum>"
for i, grad in enumerate(cur_grads):
gradlog += f"\n{str(i)}: {str(grad[1])}: {str(grad[0])}"
if grad[1] == max_grad[1]:
gradlog += "\t(*)"
logger.info(gradlog)
if np.abs(max_grad[0]) < self._threshold:
logger.info(
"Adaptive VQE terminated successfully with a final maximum gradient: %s",
str(np.abs(max_grad[0])),
)
threshold_satisfied = True
break
# check indices of picked gradients for cycles
if self._check_cyclicity(prev_op_indices):
logger.info("Alternating sequence found. Finishing.")
logger.info("Final maximum gradient: %s", str(np.abs(max_grad[0])))
alternating_sequence = True
break
# add new excitation to self._ansatz
self._excitation_list.append(max_grad[1])
theta.append(0.0)
# run VQE on current Ansatz
self._ansatz.operators = self._excitation_list
vqe.ansatz = self._ansatz
vqe.initial_point = theta
raw_vqe_result = vqe.compute_minimum_eigenvalue(self._main_operator)
theta = raw_vqe_result.optimal_point.tolist()
else:
# reached maximum number of iterations
max_iterations_exceeded = True
logger.info("Maximum number of iterations reached. Finishing.")
logger.info("Final maximum gradient: %s", str(np.abs(max_grad[0])))
# once finished evaluate auxiliary operators if any
if aux_ops is not None:
aux_values = self.evaluate_operators(raw_vqe_result.eigenstate, aux_ops)
else:
aux_values = None
raw_vqe_result.aux_operator_eigenvalues = aux_values
if threshold_satisfied:
finishing_criterion = "Threshold converged"
elif alternating_sequence:
finishing_criterion = "Aborted due to cyclicity"
elif max_iterations_exceeded:
finishing_criterion = "Maximum number of iterations reached"
else:
raise QiskitNatureError("The algorithm finished due to an unforeseen reason!")
electronic_result = problem.interpret(raw_vqe_result)
result = AdaptVQEResult()
result.combine(electronic_result)
result.num_iterations = iteration
result.final_max_gradient = max_grad[0]
result.finishing_criterion = finishing_criterion
logger.info("The final energy is: %s", str(result.computed_energies[0]))
return result