Source code for pennylane.measurements.counts
# Copyright 2018-2021 Xanadu Quantum Technologies Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This module contains the qml.counts measurement.
"""
import warnings
from typing import Sequence, Tuple, Optional
import numpy as np
import pennylane as qml
from pennylane.operation import Operator
from pennylane.wires import Wires
from .measurements import AllCounts, Counts, SampleMeasurement
from .mid_measure import MeasurementValue
[docs]def counts(op=None, wires=None, all_outcomes=False) -> "CountsMP":
r"""Sample from the supplied observable, with the number of shots
determined from the ``dev.shots`` attribute of the corresponding device,
returning the number of counts for each sample. If no observable is provided then basis state
samples are returned directly from the device.
Note that the output shape of this measurement process depends on the shots
specified on the device.
Args:
op (Observable or MeasurementValue or None): a quantum observable object. To get counts
for mid-circuit measurements, ``op`` should be a ``MeasurementValue``.
wires (Sequence[int] or int or None): the wires we wish to sample from, ONLY set wires if
op is None
all_outcomes(bool): determines whether the returned dict will contain only the observed
outcomes (default), or whether it will display all possible outcomes for the system
Returns:
CountsMP: Measurement process instance
Raises:
ValueError: Cannot set wires if an observable is provided
The samples are drawn from the eigenvalues :math:`\{\lambda_i\}` of the observable.
The probability of drawing eigenvalue :math:`\lambda_i` is given by
:math:`p(\lambda_i) = |\langle \xi_i | \psi \rangle|^2`, where :math:`| \xi_i \rangle`
is the corresponding basis state from the observable's eigenbasis.
.. note::
Differentiation of QNodes that return ``counts`` is currently not supported. Please refer to
:func:`~.pennylane.sample` if differentiability is required.
**Example**
.. code-block:: python3
dev = qml.device("default.qubit", wires=2, shots=4)
@qml.qnode(dev)
def circuit(x):
qml.RX(x, wires=0)
qml.Hadamard(wires=1)
qml.CNOT(wires=[0, 1])
return qml.counts(qml.Y(0))
Executing this QNode:
>>> circuit(0.5)
{-1: 2, 1: 2}
If no observable is provided, then the raw basis state samples obtained
from device are returned (e.g., for a qubit device, samples from the
computational device are returned). In this case, ``wires`` can be specified
so that sample results only include measurement results of the qubits of interest.
.. code-block:: python3
dev = qml.device("default.qubit", wires=2, shots=4)
@qml.qnode(dev)
def circuit(x):
qml.RX(x, wires=0)
qml.Hadamard(wires=1)
qml.CNOT(wires=[0, 1])
return qml.counts()
Executing this QNode:
>>> circuit(0.5)
{'00': 3, '01': 1}
By default, outcomes that were not observed will not be included in the dictionary.
.. code-block:: python3
dev = qml.device("default.qubit", wires=2, shots=4)
@qml.qnode(dev)
def circuit():
qml.X(0)
return qml.counts()
Executing this QNode shows only the observed outcomes:
>>> circuit()
{'10': 4}
Passing all_outcomes=True will create a dictionary that displays all possible outcomes:
.. code-block:: python3
@qml.qnode(dev)
def circuit():
qml.X(0)
return qml.counts(all_outcomes=True)
Executing this QNode shows counts for all states:
>>> circuit()
{'00': 0, '01': 0, '10': 4, '11': 0}
"""
if isinstance(op, MeasurementValue):
return CountsMP(obs=op, all_outcomes=all_outcomes)
if isinstance(op, Sequence):
if not all(isinstance(o, MeasurementValue) and len(o.measurements) == 1 for o in op):
raise qml.QuantumFunctionError(
"Only sequences of single MeasurementValues can be passed with the op argument. "
"MeasurementValues manipulated using arithmetic operators cannot be used when "
"collecting statistics for a sequence of mid-circuit measurements."
)
return CountsMP(obs=op, all_outcomes=all_outcomes)
if op is not None and not op.is_hermitian: # None type is also allowed for op
warnings.warn(f"{op.name} might not be hermitian.")
if wires is not None:
if op is not None:
raise ValueError(
"Cannot specify the wires to sample if an observable is "
"provided. The wires to sample will be determined directly from the observable."
)
wires = Wires(wires)
return CountsMP(obs=op, wires=wires, all_outcomes=all_outcomes)
[docs]class CountsMP(SampleMeasurement):
"""Measurement process that samples from the supplied observable and returns the number of
counts for each sample.
Please refer to :func:`counts` for detailed documentation.
Args:
obs (Union[.Operator, .MeasurementValue]): The observable that is to be measured
as part of the measurement process. Not all measurement processes require observables
(for example ``Probability``); this argument is optional.
wires (.Wires): The wires the measurement process applies to.
This can only be specified if an observable was not provided.
eigvals (array): A flat array representing the eigenvalues of the measurement.
This can only be specified if an observable was not provided.
id (str): custom label given to a measurement instance, can be useful for some applications
where the instance has to be identified
all_outcomes(bool): determines whether the returned dict will contain only the observed
outcomes (default), or whether it will display all possible outcomes for the system
"""
# pylint: disable=too-many-arguments
def __init__(
self,
obs: Optional[Operator] = None,
wires=None,
eigvals=None,
id: Optional[str] = None,
all_outcomes: bool = False,
):
self.all_outcomes = all_outcomes
super().__init__(obs, wires, eigvals, id)
def _flatten(self):
metadata = (("wires", self.raw_wires), ("all_outcomes", self.all_outcomes))
return (self.obs or self.mv, self._eigvals), metadata
def __repr__(self):
if self.mv:
return f"CountsMP({repr(self.mv)}, all_outcomes={self.all_outcomes})"
if self.obs:
return f"CountsMP({self.obs}, all_outcomes={self.all_outcomes})"
if self._eigvals is not None:
return f"CountsMP(eigvals={self._eigvals}, wires={self.wires.tolist()}, all_outcomes={self.all_outcomes})"
return f"CountsMP(wires={self.wires.tolist()}, all_outcomes={self.all_outcomes})"
@property
def hash(self):
"""int: returns an integer hash uniquely representing the measurement process"""
fingerprint = (
self.__class__.__name__,
getattr(self.obs, "hash", "None"),
str(self._eigvals), # eigvals() could be expensive to compute for large observables
tuple(self.wires.tolist()),
self.all_outcomes,
)
return hash(fingerprint)
@property
def return_type(self):
return AllCounts if self.all_outcomes else Counts
[docs] def process_samples(
self,
samples: Sequence[complex],
wire_order: Wires,
shot_range: Tuple[int] = None,
bin_size: int = None,
):
with qml.queuing.QueuingManager.stop_recording():
samples = qml.sample(op=self.obs or self.mv, wires=self._wires).process_samples(
samples, wire_order, shot_range, bin_size
)
if bin_size is None:
return self._samples_to_counts(samples)
num_wires = len(self.wires) if self.wires else len(wire_order)
samples = (
samples.reshape((num_wires, -1)).T.reshape(-1, bin_size, num_wires)
if self.obs is None and not isinstance(self.mv, MeasurementValue)
else samples.reshape((-1, bin_size))
)
return [self._samples_to_counts(bin_sample) for bin_sample in samples]
def _samples_to_counts(self, samples):
"""Groups the samples into a dictionary showing number of occurrences for
each possible outcome.
The format of the dictionary depends on the all_outcomes attribute. By default,
the dictionary will only contain the observed outcomes. Optionally (all_outcomes=True)
the dictionary will instead contain all possible outcomes, with a count of 0
for those not observed. See example.
Args:
samples: An array of samples, with the shape being ``(shots,len(wires))`` if an observable
is provided, with sample values being an array of 0s or 1s for each wire. Otherwise, it
has shape ``(shots,)``, with sample values being scalar eigenvalues of the observable
Returns:
dict: dictionary with format ``{'outcome': num_occurrences}``, including all
outcomes for the sampled observable
**Example**
>>> samples
tensor([[0, 0],
[0, 0],
[1, 0]], requires_grad=True)
By default, this will return:
>>> self._samples_to_counts(samples)
{'00': 2, '10': 1}
However, if ``all_outcomes=True``, this will return:
>>> self._samples_to_counts(samples)
{'00': 2, '01': 0, '10': 1, '11': 0}
The variable all_outcomes can be set when running measurements.counts, i.e.:
.. code-block:: python3
dev = qml.device("default.qubit", wires=2, shots=4)
@qml.qnode(dev)
def circuit(x):
qml.RX(x, wires=0)
return qml.counts(all_outcomes=True)
"""
outcomes = []
# if an observable was provided, batched samples will have shape (batch_size, shots)
batched_ndims = 2
shape = qml.math.shape(samples)
if self.obs is None and not isinstance(self.mv, MeasurementValue):
# convert samples and outcomes (if using) from arrays to str for dict keys
# remove nans
mask = qml.math.isnan(samples)
num_wires = shape[-1]
if np.any(mask):
mask = np.logical_not(np.any(mask, axis=tuple(range(1, samples.ndim))))
samples = samples[mask, ...]
# convert to string
def convert(x):
return f"{x:0{num_wires}b}"
exp2 = 2 ** np.arange(num_wires - 1, -1, -1)
samples = np.einsum("...i,i", samples, exp2)
new_shape = samples.shape
samples = qml.math.cast_like(samples, qml.math.int8(0))
samples = list(map(convert, samples.ravel()))
samples = np.array(samples).reshape(new_shape)
batched_ndims = 3 # no observable was provided, batched samples will have shape (batch_size, shots, len(wires))
if self.all_outcomes:
num_wires = len(self.wires) if len(self.wires) > 0 else shape[-1]
outcomes = list(map(convert, range(2**num_wires)))
elif self.all_outcomes:
# This also covers statistics for mid-circuit measurements manipulated using
# arithmetic operators
outcomes = self.eigvals()
batched = len(shape) == batched_ndims
if not batched:
samples = samples[None]
# generate empty outcome dict, populate values with state counts
base_dict = {k: qml.math.int64(0) for k in outcomes}
outcome_dicts = [base_dict.copy() for _ in range(shape[0])]
results = [qml.math.unique(batch, return_counts=True) for batch in samples]
for result, outcome_dict in zip(results, outcome_dicts):
states, _counts = result
for state, count in zip(qml.math.unwrap(states), _counts):
outcome_dict[state] = count
return outcome_dicts if batched else outcome_dicts[0]
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