def aux_operators(
self, aux_operators: Optional[
Union[OperatorBase, LegacyBaseOperator,
List[Optional[Union[OperatorBase, LegacyBaseOperator]]]]]
) -> None:
""" Set aux operators """
if aux_operators is None:
aux_operators = []
elif not isinstance(aux_operators, list):
aux_operators = [aux_operators]
# We need to handle the array entries being Optional i.e. having value None
self._aux_op_nones = [op is None for op in aux_operators]
if aux_operators:
zero_op = I.tensorpower(self.operator.num_qubits) * 0.0
converted = []
for op in aux_operators:
if op is None:
converted.append(zero_op)
elif isinstance(op, LegacyBaseOperator):
converted.append(op.to_opflow())
else:
converted.append(op)
# For some reason Chemistry passes aux_ops with 0 qubits and paulis sometimes.
aux_operators = [zero_op if op == 0 else op for op in converted]
self._aux_operators = aux_operators # type: List
def test_io_consistency(self):
""" consistency test """
new_op = X ^ Y ^ I
label = 'XYI'
# label = new_op.primitive.to_label()
self.assertEqual(str(new_op.primitive), label)
np.testing.assert_array_almost_equal(new_op.primitive.to_matrix(),
Operator.from_label(label).data)
self.assertEqual(new_op.primitive, Pauli(label))
x_mat = X.primitive.to_matrix()
y_mat = Y.primitive.to_matrix()
i_mat = np.eye(2, 2)
np.testing.assert_array_almost_equal(new_op.primitive.to_matrix(),
np.kron(np.kron(x_mat, y_mat), i_mat))
hi = np.kron(H.to_matrix(), I.to_matrix())
hi2 = Operator.from_label('HI').data
hi3 = (H ^ I).to_matrix()
np.testing.assert_array_almost_equal(hi, hi2)
np.testing.assert_array_almost_equal(hi2, hi3)
xy = np.kron(X.to_matrix(), Y.to_matrix())
xy2 = Operator.from_label('XY').data
xy3 = (X ^ Y).to_matrix()
np.testing.assert_array_almost_equal(xy, xy2)
np.testing.assert_array_almost_equal(xy2, xy3)
# Check if numpy array instantiation is the same as from Operator
matrix_op = Operator.from_label('+r')
np.testing.assert_array_almost_equal(PrimitiveOp(matrix_op).to_matrix(),
PrimitiveOp(matrix_op.data).to_matrix())
# Ditto list of lists
np.testing.assert_array_almost_equal(PrimitiveOp(matrix_op.data.tolist()).to_matrix(),
PrimitiveOp(matrix_op.data).to_matrix())
def aux_operators(
self, aux_operators: Optional[
Union[OperatorBase, LegacyBaseOperator,
List[Optional[Union[OperatorBase, LegacyBaseOperator]]]]]
) -> None:
if aux_operators is None:
aux_operators = []
elif not isinstance(aux_operators, list):
aux_operators = [aux_operators]
if aux_operators:
zero_op = I.tensorpower(self.operator.num_qubits) * 0.0
converted = [
op.to_opflow() if isinstance(op, LegacyBaseOperator) else op
for op in aux_operators
]
# For some reason Chemistry passes aux_ops with 0 qubits and paulis sometimes.
aux_operators = [zero_op if op == 0 else op for op in converted]
self._aux_operators = aux_operators
def test_is_hermitian(self):
"""Test is_hermitian method."""
with self.subTest("I"):
self.assertTrue(I.is_hermitian())
with self.subTest("X"):
self.assertTrue(X.is_hermitian())
with self.subTest("Y"):
self.assertTrue(Y.is_hermitian())
with self.subTest("Z"):
self.assertTrue(Z.is_hermitian())
with self.subTest("XY"):
self.assertFalse((X @ Y).is_hermitian())
with self.subTest("CX"):
self.assertTrue(CX.is_hermitian())
with self.subTest("T"):
self.assertFalse(T.is_hermitian())
def compute_minimum_eigenvalue(
self,
operator: OperatorBase,
aux_operators: Optional[List[Optional[OperatorBase]]] = None
) -> MinimumEigensolverResult:
super().compute_minimum_eigenvalue(operator, aux_operators)
if self.quantum_instance is None:
raise AlgorithmError(
"A QuantumInstance or Backend "
"must be supplied to run the quantum algorithm.")
if operator is None:
raise AlgorithmError("The operator was never provided.")
operator = self._check_operator(operator)
# We need to handle the array entries being Optional i.e. having value None
if aux_operators:
zero_op = I.tensorpower(operator.num_qubits) * 0.0
converted = []
for op in aux_operators:
if op is None:
converted.append(zero_op)
else:
converted.append(op)
# For some reason Chemistry passes aux_ops with 0 qubits and paulis sometimes.
aux_operators = [zero_op if op == 0 else op for op in converted]
else:
aux_operators = None
self._quantum_instance.circuit_summary = True
self._eval_count = 0
# Convert the gradient operator into a callable function that is compatible with the
# optimization routine.
if self._gradient:
if isinstance(self._gradient, GradientBase):
self._gradient = self._gradient.gradient_wrapper(
~StateFn(operator) @ StateFn(self._var_form),
bind_params=self._var_form_params,
backend=self._quantum_instance)
if not self._expect_op:
self._expect_op = self.construct_expectation(
self._var_form_params, operator)
vqresult = self.find_minimum(initial_point=self.initial_point,
var_form=self.var_form,
cost_fn=self._energy_evaluation,
gradient_fn=self._gradient,
optimizer=self.optimizer)
self._ret = VQEResult()
self._ret.combine(vqresult)
if vqresult.optimizer_evals is not None and \
self._eval_count >= vqresult.optimizer_evals:
self._eval_count = vqresult.optimizer_evals
self._eval_time = vqresult.optimizer_time
logger.info(
'Optimization complete in %s seconds.\nFound opt_params %s in %s evals',
self._eval_time, vqresult.optimal_point, self._eval_count)
self._ret.eigenvalue = vqresult.optimal_value + 0j
self._ret.eigenstate = self.get_optimal_vector()
self._ret.eigenvalue = self.get_optimal_cost()
if aux_operators:
self._eval_aux_ops(aux_operators)
self._ret.aux_operator_eigenvalues = self._ret.aux_operator_eigenvalues[
0]
self._ret.cost_function_evals = self._eval_count
return self._ret
def compute_minimum_eigenvalue(
self,
operator: OperatorBase,
aux_operators: Optional[ListOrDict[OperatorBase]] = None
) -> MinimumEigensolverResult:
super().compute_minimum_eigenvalue(operator, aux_operators)
if self.quantum_instance is None:
raise AlgorithmError(
"A QuantumInstance or Backend must be supplied to run the quantum algorithm."
)
self.quantum_instance.circuit_summary = True
# this sets the size of the ansatz, so it must be called before the initial point
# validation
self._check_operator_ansatz(operator)
# set an expectation for this algorithm run (will be reset to None at the end)
initial_point = _validate_initial_point(self.initial_point,
self.ansatz)
bounds = _validate_bounds(self.ansatz)
# We need to handle the array entries being zero or Optional i.e. having value None
if aux_operators:
zero_op = I.tensorpower(operator.num_qubits) * 0.0
# Convert the None and zero values when aux_operators is a list.
# Drop None and convert zero values when aux_operators is a dict.
if isinstance(aux_operators, list):
key_op_iterator = enumerate(aux_operators)
converted = [zero_op] * len(aux_operators)
else:
key_op_iterator = aux_operators.items()
converted = {}
for key, op in key_op_iterator:
if op is not None:
converted[key] = zero_op if op == 0 else op
aux_operators = converted
else:
aux_operators = None
# Convert the gradient operator into a callable function that is compatible with the
# optimization routine.
if isinstance(self._gradient, GradientBase):
gradient = self._gradient.gradient_wrapper(
~StateFn(operator) @ StateFn(self._ansatz),
bind_params=self._ansatz_params,
backend=self._quantum_instance,
)
else:
gradient = self._gradient
self._eval_count = 0
energy_evaluation, expectation = self.get_energy_evaluation(
operator, return_expectation=True)
start_time = time()
# keep this until Optimizer.optimize is removed
try:
opt_result = self.optimizer.minimize(fun=energy_evaluation,
x0=initial_point,
jac=gradient,
bounds=bounds)
except AttributeError:
# self.optimizer is an optimizer with the deprecated interface that uses
# ``optimize`` instead of ``minimize```
warnings.warn(
"Using an optimizer that is run with the ``optimize`` method is "
"deprecated as of Qiskit Terra 0.19.0 and will be unsupported no "
"sooner than 3 months after the release date. Instead use an optimizer "
"providing ``minimize`` (see qiskit.algorithms.optimizers.Optimizer).",
DeprecationWarning,
stacklevel=2,
)
opt_result = self.optimizer.optimize(len(initial_point),
energy_evaluation, gradient,
bounds, initial_point)
eval_time = time() - start_time
result = VQEResult()
result.optimal_point = opt_result.x
result.optimal_parameters = dict(zip(self._ansatz_params,
opt_result.x))
result.optimal_value = opt_result.fun
result.cost_function_evals = opt_result.nfev
result.optimizer_time = eval_time
result.eigenvalue = opt_result.fun + 0j
result.eigenstate = self._get_eigenstate(result.optimal_parameters)
logger.info(
"Optimization complete in %s seconds.\nFound opt_params %s in %s evals",
eval_time,
result.optimal_point,
self._eval_count,
)
# TODO delete as soon as get_optimal_vector etc are removed
self._ret = result
if aux_operators is not None:
aux_values = self._eval_aux_ops(opt_result.x,
aux_operators,
expectation=expectation)
result.aux_operator_eigenvalues = aux_values
return result
def compute_eigenvalues(
self,
operator: OperatorBase,
aux_operators: Optional[List[Optional[OperatorBase]]] = None
) -> EigensolverResult:
super().compute_eigenvalues(operator, aux_operators)
if operator is None:
raise AlgorithmError("Operator was never provided")
self._check_set_k(operator)
if aux_operators:
zero_op = I.tensorpower(operator.num_qubits) * 0.0
# For some reason Chemistry passes aux_ops with 0 qubits and paulis sometimes.
aux_operators = [
zero_op if op == 0 else op for op in aux_operators
]
else:
aux_operators = None
k_orig = self._k
if self._filter_criterion:
# need to consider all elements if a filter is set
self._k = 2**operator.num_qubits
self._ret = EigensolverResult()
self._solve(operator)
# compute energies before filtering, as this also evaluates the aux operators
self._get_energies(operator, aux_operators)
# if a filter is set, loop over the given values and only keep
if self._filter_criterion:
eigvecs = []
eigvals = []
aux_ops = []
cnt = 0
for i in range(len(self._ret.eigenvalues)):
eigvec = self._ret.eigenstates[i]
eigval = self._ret.eigenvalues[i]
if self._ret.aux_operator_eigenvalues is not None:
aux_op = self._ret.aux_operator_eigenvalues[i]
else:
aux_op = None
if self._filter_criterion(eigvec, eigval, aux_op):
cnt += 1
eigvecs += [eigvec]
eigvals += [eigval]
if self._ret.aux_operator_eigenvalues is not None:
aux_ops += [aux_op]
if cnt == k_orig:
break
self._ret.eigenstates = np.array(eigvecs)
self._ret.eigenvalues = np.array(eigvals)
# conversion to np.array breaks in case of aux_ops
self._ret.aux_operator_eigenvalues = aux_ops
self._k = k_orig
# evaluate ground state after filtering (in case a filter is set)
self._get_ground_state_energy(operator)
if self._ret.eigenstates is not None:
self._ret.eigenstates = ListOp(
[StateFn(vec) for vec in self._ret.eigenstates])
logger.debug('EigensolverResult:\n%s', self._ret)
return self._ret
def compute_minimum_eigenvalue(
self,
operator: OperatorBase,
aux_operators: Optional[List[Optional[OperatorBase]]] = None
) -> MinimumEigensolverResult:
super().compute_minimum_eigenvalue(operator, aux_operators)
if self.quantum_instance is None:
raise AlgorithmError(
"A QuantumInstance or Backend must be supplied to run the quantum algorithm."
)
self.quantum_instance.circuit_summary = True
# this sets the size of the ansatz, so it must be called before the initial point
# validation
self._check_operator_ansatz(operator)
# set an expectation for this algorithm run (will be reset to None at the end)
initial_point = _validate_initial_point(self.initial_point,
self.ansatz)
bounds = _validate_bounds(self.ansatz)
# We need to handle the array entries being Optional i.e. having value None
if aux_operators:
zero_op = I.tensorpower(operator.num_qubits) * 0.0
converted = []
for op in aux_operators:
if op is None:
converted.append(zero_op)
else:
converted.append(op)
# For some reason Chemistry passes aux_ops with 0 qubits and paulis sometimes.
aux_operators = [zero_op if op == 0 else op for op in converted]
else:
aux_operators = None
# Convert the gradient operator into a callable function that is compatible with the
# optimization routine.
if isinstance(self._gradient, GradientBase):
gradient = self._gradient.gradient_wrapper(
~StateFn(operator) @ StateFn(self._ansatz),
bind_params=self._ansatz_params,
backend=self._quantum_instance,
)
else:
gradient = self._gradient
self._eval_count = 0
energy_evaluation, expectation = self.get_energy_evaluation(
operator, return_expectation=True)
start_time = time()
opt_params, opt_value, nfev = self.optimizer.optimize(
num_vars=len(initial_point),
objective_function=energy_evaluation,
gradient_function=gradient,
variable_bounds=bounds,
initial_point=initial_point,
)
eval_time = time() - start_time
result = VQEResult()
result.optimal_point = opt_params
result.optimal_parameters = dict(zip(self._ansatz_params, opt_params))
result.optimal_value = opt_value
result.cost_function_evals = nfev
result.optimizer_time = eval_time
result.eigenvalue = opt_value + 0j
result.eigenstate = self._get_eigenstate(result.optimal_parameters)
logger.info(
"Optimization complete in %s seconds.\nFound opt_params %s in %s evals",
eval_time,
result.optimal_point,
self._eval_count,
)
# TODO delete as soon as get_optimal_vector etc are removed
self._ret = result
if aux_operators is not None:
aux_values = self._eval_aux_ops(opt_params,
aux_operators,
expectation=expectation)
result.aux_operator_eigenvalues = aux_values[0]
return result
class TestPhaseEstimation(QiskitAlgorithmsTestCase):
"""Evolution tests."""
# pylint: disable=invalid-name
def one_phase(
self,
unitary_circuit,
state_preparation=None,
backend_type=None,
phase_estimator=None,
num_iterations=6,
):
"""Run phase estimation with operator, eigenvalue pair `unitary_circuit`,
`state_preparation`. Return the estimated phase as a value in :math:`[0,1)`.
"""
if backend_type is None:
backend_type = "qasm_simulator"
backend = qiskit.BasicAer.get_backend(backend_type)
qi = qiskit.utils.QuantumInstance(backend=backend, shots=10000)
if phase_estimator is None:
phase_estimator = IterativePhaseEstimation
if phase_estimator == IterativePhaseEstimation:
p_est = IterativePhaseEstimation(num_iterations=num_iterations,
quantum_instance=qi)
elif phase_estimator == PhaseEstimation:
p_est = PhaseEstimation(num_evaluation_qubits=6,
quantum_instance=qi)
else:
raise ValueError("Unrecognized phase_estimator")
result = p_est.estimate(unitary=unitary_circuit,
state_preparation=state_preparation)
phase = result.phase
return phase
@data(
(X.to_circuit(), 0.5, "statevector_simulator",
IterativePhaseEstimation),
(X.to_circuit(), 0.5, "qasm_simulator", IterativePhaseEstimation),
(None, 0.0, "qasm_simulator", IterativePhaseEstimation),
(X.to_circuit(), 0.5, "qasm_simulator", PhaseEstimation),
(None, 0.0, "qasm_simulator", PhaseEstimation),
(X.to_circuit(), 0.5, "statevector_simulator", PhaseEstimation),
)
@unpack
def test_qpe_Z(self, state_preparation, expected_phase, backend_type,
phase_estimator):
"""eigenproblem Z, |0> and |1>"""
unitary_circuit = Z.to_circuit()
phase = self.one_phase(
unitary_circuit,
state_preparation,
backend_type=backend_type,
phase_estimator=phase_estimator,
)
self.assertEqual(phase, expected_phase)
@data(
(H.to_circuit(), 0.0, IterativePhaseEstimation),
((H @ X).to_circuit(), 0.5, IterativePhaseEstimation),
(H.to_circuit(), 0.0, PhaseEstimation),
((H @ X).to_circuit(), 0.5, PhaseEstimation),
)
@unpack
def test_qpe_X_plus_minus(self, state_preparation, expected_phase,
phase_estimator):
"""eigenproblem X, (|+>, |->)"""
unitary_circuit = X.to_circuit()
phase = self.one_phase(unitary_circuit,
state_preparation,
phase_estimator=phase_estimator)
self.assertEqual(phase, expected_phase)
@data(
(X.to_circuit(), 0.125, IterativePhaseEstimation),
(I.to_circuit(), 0.875, IterativePhaseEstimation),
(X.to_circuit(), 0.125, PhaseEstimation),
(I.to_circuit(), 0.875, PhaseEstimation),
)
@unpack
def test_qpe_RZ(self, state_preparation, expected_phase, phase_estimator):
"""eigenproblem RZ, (|0>, |1>)"""
alpha = np.pi / 2
unitary_circuit = QuantumCircuit(1)
unitary_circuit.rz(alpha, 0)
phase = self.one_phase(unitary_circuit,
state_preparation,
phase_estimator=phase_estimator)
self.assertEqual(phase, expected_phase)
def test_check_num_iterations(self):
"""test check for num_iterations greater than zero"""
unitary_circuit = X.to_circuit()
state_preparation = None
with self.assertRaises(ValueError):
self.one_phase(unitary_circuit,
state_preparation,
num_iterations=-1)
def phase_estimation(
self,
unitary_circuit,
state_preparation=None,
num_evaluation_qubits=6,
backend=None,
construct_circuit=False,
):
"""Run phase estimation with operator, eigenvalue pair `unitary_circuit`,
`state_preparation`. Return all results
"""
if backend is None:
backend = qiskit.BasicAer.get_backend("statevector_simulator")
qi = qiskit.utils.QuantumInstance(backend=backend, shots=10000)
phase_est = PhaseEstimation(
num_evaluation_qubits=num_evaluation_qubits, quantum_instance=qi)
if construct_circuit:
pe_circuit = phase_est.construct_circuit(unitary_circuit,
state_preparation)
result = phase_est.estimate_from_pe_circuit(
pe_circuit, unitary_circuit.num_qubits)
else:
result = phase_est.estimate(unitary=unitary_circuit,
state_preparation=state_preparation)
return result
@data(True, False)
def test_qpe_Zplus(self, construct_circuit):
"""superposition eigenproblem Z, |+>"""
unitary_circuit = Z.to_circuit()
state_preparation = H.to_circuit() # prepare |+>
result = self.phase_estimation(
unitary_circuit,
state_preparation,
backend=qiskit.BasicAer.get_backend("statevector_simulator"),
construct_circuit=construct_circuit,
)
phases = result.filter_phases(1e-15, as_float=True)
with self.subTest("test phases has correct values"):
self.assertEqual(list(phases.keys()), [0.0, 0.5])
with self.subTest("test phases has correct probabilities"):
np.testing.assert_allclose(list(phases.values()), [0.5, 0.5])
with self.subTest("test bitstring representation"):
phases = result.filter_phases(1e-15, as_float=False)
self.assertEqual(list(phases.keys()), ["000000", "100000"])
