def _recursive_convert(self, operator: OperatorBase) -> OperatorBase:
if isinstance(operator, EvolvedOp):
if isinstance(operator.primitive, (PauliOp, PauliSumOp)):
pauli = operator.primitive.primitive
time = operator.coeff * operator.primitive.coeff
evo = PauliEvolutionGate(
pauli,
time=time,
synthesis=self._get_evolution_synthesis())
return CircuitOp(evo.definition)
# operator = EvolvedOp(operator.primitive.to_pauli_op(), coeff=operator.coeff)
if not {"Pauli"} == operator.primitive_strings():
logger.warning(
"Evolved Hamiltonian is not composed of only Paulis, converting to "
"Pauli representation, which can be expensive.")
# Setting massive=False because this conversion is implicit. User can perform this
# action on the Hamiltonian with massive=True explicitly if they so choose.
# TODO explore performance to see whether we should avoid doing this repeatedly
pauli_ham = operator.primitive.to_pauli_op(massive=False)
operator = EvolvedOp(pauli_ham, coeff=operator.coeff)
if isinstance(operator.primitive, SummedOp):
# TODO uncomment when we implement Abelian grouped evolution.
# if operator.primitive.abelian:
# return self.evolution_for_abelian_paulisum(operator.primitive)
# else:
# Collect terms that are not the identity.
oplist = [
x for x in operator.primitive
if not isinstance(x, PauliOp) or sum(x.primitive.x +
x.primitive.z) != 0
]
# Collect the coefficients of any identity terms,
# which become global phases when exponentiated.
identity_phases = [
x.coeff for x in operator.primitive
if isinstance(x, PauliOp) and sum(x.primitive.x +
x.primitive.z) == 0
]
# Construct sum without the identity operators.
new_primitive = SummedOp(oplist,
coeff=operator.primitive.coeff)
trotterized = self.trotter.convert(new_primitive)
circuit_no_identities = self._recursive_convert(trotterized)
# Set the global phase of the QuantumCircuit to account for removed identity terms.
global_phase = -sum(identity_phases) * operator.primitive.coeff
circuit_no_identities.primitive.global_phase = global_phase
return circuit_no_identities
# Covers ListOp, ComposedOp, TensoredOp
elif isinstance(operator.primitive, ListOp):
converted_ops = [
self._recursive_convert(op)
for op in operator.primitive.oplist
]
return operator.primitive.__class__(converted_ops,
coeff=operator.coeff)
elif isinstance(operator, ListOp):
return operator.traverse(self.convert).reduce()
return operator
def build(operator: OperatorBase = None) -> EvolutionBase:
r"""
A factory method for convenient automatic selection of an Evolution algorithm based on the
Operator to be converted.
Args:
operator: the Operator being evolved
Returns:
EvolutionBase: the ``EvolutionBase`` best suited to evolve operator.
Raises:
ValueError: If operator is not of a composition for which we know the best Evolution
method.
"""
primitive_strings = operator.primitive_strings()
if "Matrix" in primitive_strings:
return MatrixEvolution()
elif "Pauli" in primitive_strings or "SparsePauliOp" in primitive_strings:
# TODO figure out what to do based on qubits and hamming weight.
return PauliTrotterEvolution()
else:
raise ValueError("Evolutions of mixed Operators not yet supported.")
def convert(self, operator: OperatorBase) -> OperatorBase:
r"""
Traverse the operator, replacing ``EvolvedOps`` with ``CircuitOps`` containing
``UnitaryGates`` or ``HamiltonianGates`` (if self.coeff is a ``ParameterExpression``)
equalling the exponentiation of -i * operator. This is done by converting the
``EvolvedOp.primitive`` to a ``MatrixOp`` and simply calling ``.exp_i()`` on that.
Args:
operator: The Operator to convert.
Returns:
The converted operator.
"""
if isinstance(operator, EvolvedOp):
if not {"Matrix"} == operator.primitive_strings():
logger.warning(
"Evolved Hamiltonian is not composed of only MatrixOps, converting "
"to Matrix representation, which can be expensive.")
# Setting massive=False because this conversion is implicit. User can perform this
# action on the Hamiltonian with massive=True explicitly if they so choose.
# TODO explore performance to see whether we should avoid doing this repeatedly
matrix_ham = operator.primitive.to_matrix_op(massive=False)
operator = EvolvedOp(matrix_ham, coeff=operator.coeff)
if isinstance(operator.primitive, ListOp):
return operator.primitive.exp_i() * operator.coeff
elif isinstance(operator.primitive, (MatrixOp, PauliOp)):
return operator.primitive.exp_i()
elif isinstance(operator, ListOp):
return operator.traverse(self.convert).reduce()
return operator
def convert(self, operator: OperatorBase) -> OperatorBase:
"""Convert the Operator to ``CircuitStateFns``, recursively if ``traverse`` is True.
Args:
operator: The Operator to convert
Returns:
The converted Operator.
"""
if isinstance(operator, DictStateFn) and self._convert_dicts:
return CircuitStateFn.from_dict(operator.primitive)
if isinstance(operator, VectorStateFn) and self._convert_vectors:
return CircuitStateFn.from_vector(operator.to_matrix(massive=True))
elif isinstance(operator,
ListOp) and "Dict" in operator.primitive_strings():
return operator.traverse(self.convert)
else:
return operator
def convert(self, operator: OperatorBase) -> OperatorBase:
"""Accepts an Operator and returns a new Operator with the Pauli measurements replaced by
diagonal Pauli post-rotation based measurements so they can be evaluated by sampling and
averaging.
Args:
operator: The operator to convert.
Returns:
The converted operator.
"""
if isinstance(operator, ListOp):
return operator.traverse(self.convert).reduce()
if isinstance(operator, OperatorStateFn) and operator.is_measurement:
# Change to Pauli representation if necessary
if (isinstance(operator.primitive, (ListOp, PrimitiveOp))
and not isinstance(operator.primitive, PauliSumOp) and
{"Pauli", "SparsePauliOp"} < operator.primitive_strings()):
logger.warning(
"Measured Observable is not composed of only Paulis, converting to "
"Pauli representation, which can be expensive.")
# Setting massive=False because this conversion is implicit. User can perform this
# action on the Observable with massive=True explicitly if they so choose.
pauli_obsv = operator.primitive.to_pauli_op(massive=False)
operator = StateFn(pauli_obsv,
is_measurement=True,
coeff=operator.coeff)
if self._grouper and isinstance(operator.primitive,
(ListOp, PauliSumOp)):
grouped = self._grouper.convert(operator.primitive)
operator = StateFn(grouped,
is_measurement=True,
coeff=operator.coeff)
# Convert the measurement into diagonal basis (PauliBasisChange chooses
# this basis by default).
cob = PauliBasisChange(
replacement_fn=PauliBasisChange.measurement_replacement_fn)
return cob.convert(operator).reduce()
return operator
def convert(self, operator: OperatorBase) -> OperatorBase:
r"""
Given a ``PauliOp``, or an Operator containing ``PauliOps`` if ``_traverse`` is True,
converts each Pauli into the basis specified by self._destination and a
basis-change-circuit, calls ``replacement_fn`` with these two Operators, and replaces
the ``PauliOps`` with the output of ``replacement_fn``. For example, for the built-in
``operator_replacement_fn`` below, each PauliOp p will be replaced by the composition
of the basis-change Clifford ``CircuitOp`` c with the destination PauliOp d and c†,
such that p = c·d·c†, up to global phase.
Args:
operator: The Operator to convert.
Returns:
The converted Operator.
"""
if (isinstance(operator, OperatorStateFn)
and isinstance(operator.primitive, PauliSumOp)
and operator.primitive.grouping_type == "TPB"):
primitive = operator.primitive.primitive.copy()
origin_x = reduce(np.logical_or, primitive.table.X)
origin_z = reduce(np.logical_or, primitive.table.Z)
origin_pauli = Pauli((origin_z, origin_x))
cob_instr_op, _ = self.get_cob_circuit(origin_pauli)
primitive.table.Z = np.logical_or(primitive.table.X,
primitive.table.Z)
primitive.table.X = False
dest_pauli_sum_op = PauliSumOp(primitive,
coeff=operator.coeff,
grouping_type="TPB")
return self._replacement_fn(cob_instr_op, dest_pauli_sum_op)
if (isinstance(operator, OperatorStateFn)
and isinstance(operator.primitive, SummedOp) and all(
isinstance(op, PauliSumOp) and op.grouping_type == "TPB"
for op in operator.primitive.oplist)):
sf_list: List[OperatorBase] = [
StateFn(op, is_measurement=operator.is_measurement)
for op in operator.primitive.oplist
]
listop_of_statefns = SummedOp(oplist=sf_list, coeff=operator.coeff)
return listop_of_statefns.traverse(self.convert)
if isinstance(operator, OperatorStateFn) and isinstance(
operator.primitive, PauliSumOp):
operator = OperatorStateFn(
operator.primitive.to_pauli_op(),
coeff=operator.coeff,
is_measurement=operator.is_measurement,
)
if isinstance(operator, PauliSumOp):
operator = operator.to_pauli_op()
if isinstance(operator, (Pauli, PauliOp)):
cob_instr_op, dest_pauli_op = self.get_cob_circuit(operator)
return self._replacement_fn(cob_instr_op, dest_pauli_op)
if isinstance(operator,
StateFn) and "Pauli" in operator.primitive_strings():
# If the StateFn/Meas only contains a Pauli, use it directly.
if isinstance(operator.primitive, PauliOp):
cob_instr_op, dest_pauli_op = self.get_cob_circuit(
operator.primitive)
return self._replacement_fn(cob_instr_op,
dest_pauli_op * operator.coeff)
# TODO make a canonical "distribute" or graph swap as method in ListOp?
elif operator.primitive.distributive:
if operator.primitive.abelian:
origin_pauli = self.get_tpb_pauli(operator.primitive)
cob_instr_op, _ = self.get_cob_circuit(origin_pauli)
diag_ops: List[OperatorBase] = [
self.get_diagonal_pauli_op(op)
for op in operator.primitive.oplist
]
dest_pauli_op = operator.primitive.__class__(
diag_ops, coeff=operator.coeff, abelian=True)
return self._replacement_fn(cob_instr_op, dest_pauli_op)
else:
sf_list = [
StateFn(op, is_measurement=operator.is_measurement)
for op in operator.primitive.oplist
]
listop_of_statefns = operator.primitive.__class__(
oplist=sf_list, coeff=operator.coeff)
return listop_of_statefns.traverse(self.convert)
elif (isinstance(operator, ListOp) and self._traverse
and "Pauli" in operator.primitive_strings()):
# If ListOp is abelian we can find a single post-rotation circuit
# for the whole set. For now,
# assume operator can only be abelian if all elements are
# Paulis (enforced in AbelianGrouper).
if operator.abelian:
origin_pauli = self.get_tpb_pauli(operator)
cob_instr_op, _ = self.get_cob_circuit(origin_pauli)
oplist = cast(List[PauliOp], operator.oplist)
diag_ops = [self.get_diagonal_pauli_op(op) for op in oplist]
dest_list_op = operator.__class__(diag_ops,
coeff=operator.coeff,
abelian=True)
return self._replacement_fn(cob_instr_op, dest_list_op)
else:
return operator.traverse(self.convert)
return operator
def build(operator: OperatorBase,
backend: Optional[Union[Backend, BaseBackend, QuantumInstance]] = None,
include_custom: bool = True) -> ExpectationBase:
"""
A factory method for convenient automatic selection of an Expectation based on the
Operator to be converted and backend used to sample the expectation value.
Args:
operator: The Operator whose expectation value will be taken.
backend: The backend which will be used to sample the expectation value.
include_custom: Whether the factory will include the (Aer) specific custom
expectations if their behavior against the backend might not be as expected.
For instance when using Aer qasm_simulator with paulis the Aer snapshot can
be used but the outcome lacks shot noise and hence does not intuitively behave
overall as people might expect when choosing a qasm_simulator. It is however
fast as long as the more state vector like behavior is acceptable.
Returns:
The expectation algorithm which best fits the Operator and backend.
Raises:
ValueError: If operator is not of a composition for which we know the best Expectation
method.
"""
backend_to_check = backend.backend if isinstance(backend, QuantumInstance) else backend
# pylint: disable=cyclic-import
primitives = operator.primitive_strings()
if primitives in ({'Pauli'}, {'SparsePauliOp'}):
if backend_to_check is None:
# If user has Aer but didn't specify a backend, use the Aer fast expectation
if has_aer():
from qiskit import Aer
backend_to_check = Aer.get_backend('qasm_simulator')
# If user doesn't have Aer, use statevector_simulator
# for < 16 qubits, and qasm with warning for more.
else:
if operator.num_qubits <= 16:
backend_to_check = BasicAer.get_backend('statevector_simulator')
else:
logger.warning(
'%d qubits is a very large expectation value. '
'Consider installing Aer to use '
'Aer\'s fast expectation, which will perform better here. We\'ll use '
'the BasicAer qasm backend for this expectation to avoid having to '
'construct the %dx%d operator matrix.',
operator.num_qubits,
2 ** operator.num_qubits,
2 ** operator.num_qubits)
backend_to_check = BasicAer.get_backend('qasm_simulator')
# If the user specified Aer qasm backend and is using a
# Pauli operator, use the Aer fast expectation if we are including such
# custom behaviors.
if is_aer_qasm(backend_to_check) and include_custom:
return AerPauliExpectation()
# If the user specified a statevector backend (either Aer or BasicAer),
# use a converter to produce a
# Matrix operator and compute using matmul
elif is_statevector_backend(backend_to_check):
if operator.num_qubits >= 16:
logger.warning(
'Note: Using a statevector_simulator with %d qubits can be very expensive. '
'Consider using the Aer qasm_simulator instead to take advantage of Aer\'s '
'built-in fast Pauli Expectation', operator.num_qubits)
return MatrixExpectation()
# All other backends, including IBMQ, BasicAer QASM, go here.
else:
return PauliExpectation()
elif primitives == {'Matrix'}:
return MatrixExpectation()
else:
raise ValueError('Expectations of Mixed Operators not yet supported.')