def __init__(self, group_paulis: bool = True) -> None:
"""
Args:
group_paulis: Whether to group the Pauli measurements into commuting sums, which all
have the same diagonalizing circuit.
"""
self._grouper = AbelianGrouper() if group_paulis else None
class PauliExpectation(ExpectationBase):
r"""
An Expectation converter for Pauli-basis observables by changing Pauli measurements to a
diagonal ({Z, I}^n) basis and appending circuit post-rotations to the measured state function.
Optionally groups the Paulis with the same post-rotations (those that commute with one
another, or form Abelian groups) into single measurements to reduce circuit execution
overhead.
"""
def __init__(self, group_paulis: bool = True) -> None:
"""
Args:
group_paulis: Whether to group the Pauli measurements into commuting sums, which all
have the same diagonalizing circuit.
"""
self._grouper = AbelianGrouper() if group_paulis else None
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 compute_variance(
self, exp_op: OperatorBase) -> Union[list, float, np.ndarray]:
def sum_variance(operator):
if isinstance(operator, ComposedOp):
sfdict = operator.oplist[1]
measurement = operator.oplist[0]
average = measurement.eval(sfdict)
variance = sum((v * (measurement.eval(b) - average))**2
for (b, v) in sfdict.primitive.items())
return operator.coeff * variance
elif isinstance(operator, ListOp):
return operator.combo_fn(
[sum_variance(op) for op in operator.oplist])
return 0.0
return sum_variance(exp_op)