Source code for pennylane.measurements.measurements
# 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 functions for computing different types of measurement
outcomes from quantum observables - expectation values, variances of expectations,
and measurement samples using AnnotatedQueues.
"""
import copy
import warnings
from abc import ABC, abstractmethod
from collections.abc import Sequence
from enum import Enum
from typing import Optional, Union
import pennylane as qml
from pennylane.math.utils import is_abstract
from pennylane.operation import DecompositionUndefinedError, EigvalsUndefinedError, Operator
from pennylane.pytrees import register_pytree
from pennylane.typing import TensorLike
from pennylane.wires import Wires
# =============================================================================
# ObservableReturnTypes types
# =============================================================================
class ObservableReturnTypes(Enum):
"""Enumeration class to represent the return types of an observable."""
Sample = "sample"
Counts = "counts"
AllCounts = "allcounts"
Variance = "var"
Expectation = "expval"
Probability = "probs"
State = "state"
MidMeasure = "measure"
VnEntropy = "vnentropy"
MutualInfo = "mutualinfo"
Shadow = "shadow"
ShadowExpval = "shadowexpval"
Purity = "purity"
def __repr__(self):
"""String representation of the return types."""
return str(self.value)
Sample = ObservableReturnTypes.Sample
"""Enum: An enumeration which represents sampling an observable."""
Counts = ObservableReturnTypes.Counts
"""Enum: An enumeration which represents returning the number of times
each of the observed outcomes occurred in sampling."""
AllCounts = ObservableReturnTypes.AllCounts
"""Enum: An enumeration which represents returning the number of times
each of the possible outcomes occurred in sampling, including 0 counts
for unobserved outcomes."""
Variance = ObservableReturnTypes.Variance
"""Enum: An enumeration which represents returning the variance of
an observable on specified wires."""
Expectation = ObservableReturnTypes.Expectation
"""Enum: An enumeration which represents returning the expectation
value of an observable on specified wires."""
Probability = ObservableReturnTypes.Probability
"""Enum: An enumeration which represents returning probabilities
of all computational basis states."""
State = ObservableReturnTypes.State
"""Enum: An enumeration which represents returning the state in the computational basis."""
MidMeasure = ObservableReturnTypes.MidMeasure
"""Enum: An enumeration which represents returning sampling the computational
basis in the middle of the circuit."""
VnEntropy = ObservableReturnTypes.VnEntropy
"""Enum: An enumeration which represents returning Von Neumann entropy before measurements."""
MutualInfo = ObservableReturnTypes.MutualInfo
"""Enum: An enumeration which represents returning the mutual information before measurements."""
Shadow = ObservableReturnTypes.Shadow
"""Enum: An enumeration which represents returning the bitstrings and recipes from
the classical shadow protocol"""
ShadowExpval = ObservableReturnTypes.ShadowExpval
"""Enum: An enumeration which represents returning the estimated expectation value
from a classical shadow measurement"""
Purity = ObservableReturnTypes.Purity
"""Enum: An enumeration which represents returning the purity of the system prior ot measurement."""
class MeasurementShapeError(ValueError):
"""An error raised when an unsupported operation is attempted with a
quantum tape."""
[docs]class MeasurementProcess(ABC, metaclass=qml.capture.ABCCaptureMeta):
"""Represents a measurement process occurring at the end of a
quantum variational circuit.
Args:
obs (Union[.Operator, .MeasurementValue, Sequence[.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
"""
# pylint:disable=too-many-instance-attributes
_shortname = None
_obs_primitive: Optional["jax.core.Primitive"] = None
_wires_primitive: Optional["jax.core.Primitive"] = None
_mcm_primitive: Optional["jax.core.Primitive"] = None
def __init_subclass__(cls, **_):
register_pytree(cls, cls._flatten, cls._unflatten)
name = getattr(cls._shortname, "value", cls.__name__)
cls._wires_primitive = qml.capture.create_measurement_wires_primitive(cls, name=name)
cls._obs_primitive = qml.capture.create_measurement_obs_primitive(cls, name=name)
cls._mcm_primitive = qml.capture.create_measurement_mcm_primitive(cls, name=name)
@classmethod
def _primitive_bind_call(cls, obs=None, wires=None, eigvals=None, id=None, **kwargs):
"""Called instead of ``type.__call__`` if ``qml.capture.enabled()``.
Measurements have three "modes":
1) Wires or wires + eigvals
2) Observable
3) Mid circuit measurements
Not all measurements support all three modes. For example, ``VNEntropyMP`` does not
allow being specified via an observable. But we handle the generic case here.
"""
if cls._obs_primitive is None:
# safety check if primitives aren't set correctly.
return type.__call__(cls, obs=obs, wires=wires, eigvals=eigvals, id=id, **kwargs)
if obs is None:
wires = () if wires is None else wires
if eigvals is None:
return cls._wires_primitive.bind(*wires, **kwargs) # wires
return cls._wires_primitive.bind(
*wires, eigvals, has_eigvals=True, **kwargs
) # wires + eigvals
if isinstance(obs, Operator) or isinstance(
getattr(obs, "aval", None), qml.capture.AbstractOperator
):
return cls._obs_primitive.bind(obs, **kwargs)
if isinstance(obs, (list, tuple)):
return cls._mcm_primitive.bind(*obs, single_mcm=False, **kwargs) # iterable of mcms
return cls._mcm_primitive.bind(obs, single_mcm=True, **kwargs) # single mcm
# pylint: disable=unused-argument
@classmethod
def _abstract_eval(
cls,
n_wires: Optional[int] = None,
has_eigvals=False,
shots: Optional[int] = None,
num_device_wires: int = 0,
) -> tuple[tuple, type]:
"""Calculate the shape and dtype that will be returned when a measurement is performed.
This information is similar to ``numeric_type`` and ``shape``, but is provided through
a class method and does not require the creation of an instance.
Note that ``shots`` should strictly be ``None`` or ``int``. Shot vectors are handled higher
in the stack.
If ``n_wires is None``, then the measurement process contains an observable. An integer
``n_wires`` can correspond either to the number of wires or to the number of mid circuit
measurements. ``n_wires = 0`` indicates a measurement that is broadcasted across all device wires.
>>> ProbabilityMP._abstract_eval(n_wires=2)
((4,), float)
>>> ProbabilityMP._abstract_eval(n_wires=0, num_device_wires=2)
((4,), float)
>>> SampleMP._abstract_eval(n_wires=0, shots=50, num_device_wires=2)
((50, 2), int)
>>> SampleMP._abstract_eval(n_wires=4, has_eigvals=True, shots=50)
((50,), float)
>>> SampleMP._abstract_eval(n_wires=None, shots=50)
((50,), float)
"""
return (), float
def _flatten(self):
metadata = (("wires", self.raw_wires),)
return (self.obs or self.mv, self._eigvals), metadata
@classmethod
def _unflatten(cls, data, metadata):
if data[0] is not None:
return cls(obs=data[0], **dict(metadata))
if data[1] is not None:
return cls(eigvals=data[1], **dict(metadata))
return cls(**dict(metadata))
# pylint: disable=too-many-arguments
def __init__(
self,
obs: Optional[
Union[
Operator,
"qml.measurements.MeasurementValue",
Sequence["qml.measurements.MeasurementValue"],
]
] = None,
wires: Optional[Wires] = None,
eigvals: Optional[TensorLike] = None,
id: Optional[str] = None,
):
if getattr(obs, "name", None) == "MeasurementValue" or isinstance(obs, Sequence):
# Cast sequence of measurement values to list
self.mv = obs if getattr(obs, "name", None) == "MeasurementValue" else list(obs)
self.obs = None
elif is_abstract(obs): # Catalyst program with qml.sample(m, wires=i)
self.mv = obs
self.obs = None
else:
self.obs = obs
self.mv = None
self.id = id
if wires is not None:
if not qml.capture.enabled() and len(wires) == 0:
raise ValueError("Cannot set an empty list of wires.")
if obs is not None:
raise ValueError("Cannot set the wires if an observable is provided.")
# _wires = None indicates broadcasting across all available wires.
# It translates to the public property wires = Wires([])
self._wires = wires
self._eigvals = None
if eigvals is not None:
if obs is not None:
raise ValueError("Cannot set the eigenvalues if an observable is provided.")
self._eigvals = qml.math.asarray(eigvals)
# Queue the measurement process
self.queue()
@property
def return_type(self) -> Optional[ObservableReturnTypes]:
"""Measurement return type."""
warnings.warn(
"MeasurementProcess property return_type is deprecated and will be removed in version 0.42. "
"Instead, please use isinstance for type checking directly.",
qml.PennyLaneDeprecationWarning,
)
return self._shortname
@property
def numeric_type(self) -> type:
"""The Python numeric type of the measurement result.
Returns:
type: The output numeric type; ``int``, ``float`` or ``complex``.
Raises:
QuantumFunctionError: the return type of the measurement process is
unrecognized and cannot deduce the numeric type
"""
raise qml.QuantumFunctionError(
f"The numeric type of the measurement {self.__class__.__name__} is not defined."
)
[docs] def shape(self, shots: Optional[int] = None, num_device_wires: int = 0) -> tuple[int, ...]:
"""Calculate the shape of the result object tensor.
Args:
shots (Optional[int]) = None: the number of shots used execute the circuit. ``None``
indicates an analytic simulation. Shot vectors are handled by calling this method
multiple times.
num_device_wires (int)=0 : The number of wires that will be used if the measurement is
broadcasted across all available wires (``len(mp.wires) == 0``). If the device
itself doesn't provide a number of wires, the number of tape wires will be provided
here instead:
Returns:
tuple[int,...]: An arbitrary length tuple of ints. May be an empty tuple.
>>> qml.probs(wires=(0,1)).shape()
(4,)
>>> qml.sample(wires=(0,1)).shape(shots=50)
(50, 2)
>>> qml.state().shape(num_device_wires=4)
(16,)
>>> qml.expval(qml.Z(0)).shape()
()
"""
raise qml.QuantumFunctionError(
f"The shape of the measurement {self.__class__.__name__} is not defined"
)
[docs] @qml.QueuingManager.stop_recording()
def diagonalizing_gates(self):
"""Returns the gates that diagonalize the measured wires such that they
are in the eigenbasis of the circuit observables.
Returns:
List[.Operation]: the operations that diagonalize the observables
"""
return self.obs.diagonalizing_gates() if self.obs else []
def __eq__(self, other):
return qml.equal(self, other)
def __hash__(self):
return self.hash
def __repr__(self):
"""Representation of this class."""
if self._shortname:
if isinstance(self._shortname, str):
name_str = self._shortname
else:
name_str = self._shortname.value
else:
name_str = type(self).__name__
if self.mv is not None:
return f"{name_str}({repr(self.mv)})"
if self.obs:
return f"{name_str}({self.obs})"
if self._eigvals is not None:
return f"{name_str}(eigvals={self._eigvals}, wires={self.wires.tolist()})"
# Todo: when tape is core the return type will always be taken from the MeasurementProcess
return f"{getattr(self._shortname, 'value', 'None')}(wires={self.wires.tolist()})"
def __copy__(self):
cls = self.__class__
copied_m = cls.__new__(cls)
for attr, value in vars(self).items():
setattr(copied_m, attr, value)
if self.obs is not None:
copied_m.obs = copy.copy(self.obs)
return copied_m
@property
def wires(self):
r"""The wires the measurement process acts on.
This is the union of all the Wires objects of the measurement.
"""
if self.mv is not None and not is_abstract(self.mv):
if isinstance(self.mv, list):
return qml.wires.Wires.all_wires([m.wires for m in self.mv])
return self.mv.wires
if self.obs is not None:
return self.obs.wires
return (
Wires.all_wires(self._wires)
if isinstance(self._wires, (tuple, list))
else self._wires or Wires([])
)
@property
def raw_wires(self):
r"""The wires the measurement process acts on.
For measurements involving more than one set of wires (such as
mutual information), this is a list of the Wires objects. Otherwise,
this is the same as :func:`~.MeasurementProcess.wires`
"""
return self._wires
[docs] def eigvals(self):
r"""Eigenvalues associated with the measurement process.
If the measurement process has an associated observable,
the eigenvalues will correspond to this observable. Otherwise,
they will be the eigenvalues provided when the measurement
process was instantiated.
Note that the eigenvalues are not guaranteed to be in any
particular order.
**Example:**
>>> m = MeasurementProcess(Expectation, obs=qml.X(1))
>>> m.eigvals()
array([1, -1])
Returns:
array: eigvals representation
"""
if self.mv is not None:
if getattr(self.mv, "name", None) == "MeasurementValue":
# "Eigvals" should be the processed values for all branches of a MeasurementValue
_, processed_values = tuple(zip(*self.mv.items()))
interface = qml.math.get_deep_interface(processed_values)
return qml.math.asarray(processed_values, like=interface)
return qml.math.arange(0, 2 ** len(self.wires), 1)
if self.obs is not None:
try:
return qml.eigvals(self.obs)
except DecompositionUndefinedError as e:
raise EigvalsUndefinedError from e
return self._eigvals
@property
def has_decomposition(self):
r"""Bool: Whether or not the MeasurementProcess returns a defined decomposition
when calling ``expand``.
"""
# If self.obs is not None, `expand` queues the diagonalizing gates of self.obs,
# which we have to check to be defined. The subsequent creation of the new
# `MeasurementProcess` within `expand` should never fail with the given parameters.
return self.obs.has_diagonalizing_gates if self.obs is not None else False
@property
def samples_computational_basis(self):
r"""Bool: Whether or not the MeasurementProcess measures in the computational basis."""
return self.obs is None
[docs] def expand(self):
"""Expand the measurement of an observable to a unitary
rotation and a measurement in the computational basis.
Returns:
.QuantumTape: a quantum tape containing the operations
required to diagonalize the observable
**Example:**
Consider a measurement process consisting of the expectation
value of an Hermitian observable:
>>> H = np.array([[1, 2], [2, 4]])
>>> obs = qml.Hermitian(H, wires=['a'])
>>> m = MeasurementProcess(Expectation, obs=obs)
Expanding this out:
>>> tape = m.expand()
We can see that the resulting tape has the qubit unitary applied,
and a measurement process with no observable, but the eigenvalues
specified:
>>> print(tape.operations)
[QubitUnitary(array([[-0.89442719, 0.4472136 ],
[ 0.4472136 , 0.89442719]]), wires=['a'])]
>>> print(tape.measurements[0].eigvals())
[0. 5.]
>>> print(tape.measurements[0].obs)
None
"""
if self.obs is None:
raise qml.operation.DecompositionUndefinedError
with qml.queuing.AnnotatedQueue() as q:
self.obs.diagonalizing_gates()
self.__class__(wires=self.obs.wires, eigvals=self.obs.eigvals())
return qml.tape.QuantumScript.from_queue(q)
[docs] def queue(self, context=qml.QueuingManager):
"""Append the measurement process to an annotated queue."""
if self.obs is not None:
context.remove(self.obs)
context.append(self)
return self
@property
def _queue_category(self):
"""Denotes that `MeasurementProcess` objects should be processed into the `_measurements` list
in `QuantumTape` objects.
This property is a temporary solution that should not exist long-term and should not be
used outside of ``QuantumTape._process_queue``.
"""
return "_measurements"
@property
def hash(self):
"""int: returns an integer hash uniquely representing the measurement process"""
fingerprint = (
self.__class__.__name__,
getattr(self.obs, "hash", "None"),
getattr(self.mv, "hash", "None"),
str(self._eigvals), # eigvals() could be expensive to compute for large observables
tuple(self.wires.tolist()),
)
return hash(fingerprint)
[docs] def simplify(self):
"""Reduce the depth of the observable to the minimum.
Returns:
.MeasurementProcess: A measurement process with a simplified observable.
"""
return self if self.obs is None else self.__class__(obs=self.obs.simplify())
# pylint: disable=protected-access
[docs] def map_wires(self, wire_map: dict):
"""Returns a copy of the current measurement process with its wires changed according to
the given wire map.
Args:
wire_map (dict): dictionary containing the old wires as keys and the new wires as values
Returns:
.MeasurementProcess: new measurement process
"""
new_measurement = copy.copy(self)
if self.mv is not None:
new_measurement.mv = (
self.mv.map_wires(wire_map=wire_map)
if getattr(self.mv, "name", None) == "MeasurementValue"
else [m.map_wires(wire_map=wire_map) for m in self.mv]
)
elif self.obs is not None:
new_measurement.obs = self.obs.map_wires(wire_map=wire_map)
elif self._wires is not None:
new_measurement._wires = Wires([wire_map.get(wire, wire) for wire in self.wires])
return new_measurement
[docs]class SampleMeasurement(MeasurementProcess):
"""Sample-based measurement process.
Any class inheriting from ``SampleMeasurement`` should define its own ``process_samples`` method,
which should have the following arguments:
* samples (Sequence[complex]): computational basis samples generated for all wires
* wire_order (Wires): wires determining the subspace that ``samples`` acts on
* shot_range (tuple[int]): 2-tuple of integers specifying the range of samples
to use. If not specified, all samples are used.
* bin_size (int): Divides the shot range into bins of size ``bin_size``, and
returns the measurement statistic separately over each bin. If not
provided, the entire shot range is treated as a single bin.
**Example:**
Let's create a measurement that returns the sum of all samples of the given wires.
>>> class MyMeasurement(SampleMeasurement):
... def process_samples(self, samples, wire_order, shot_range=None, bin_size=None):
... return qml.math.sum(samples[..., self.wires])
We can now execute it in a QNode:
>>> dev = qml.device("default.qubit", wires=2, shots=1000)
>>> @qml.qnode(dev)
... def circuit():
... qml.X(0)
... return MyMeasurement(wires=[0]), MyMeasurement(wires=[1])
>>> circuit()
(tensor(1000, requires_grad=True), tensor(0, requires_grad=True))
"""
_shortname = Sample #! Note: deprecated. Change the value to "sample" in v0.42
[docs] @abstractmethod
def process_samples(
self,
samples: Sequence[complex],
wire_order: Wires,
shot_range: tuple[int] = None,
bin_size: int = None,
):
"""Process the given samples.
Args:
samples (Sequence[complex]): computational basis samples generated for all wires
wire_order (Wires): wires determining the subspace that ``samples`` acts on
shot_range (tuple[int]): 2-tuple of integers specifying the range of samples
to use. If not specified, all samples are used.
bin_size (int): Divides the shot range into bins of size ``bin_size``, and
returns the measurement statistic separately over each bin. If not
provided, the entire shot range is treated as a single bin.
"""
[docs] @abstractmethod
def process_counts(self, counts: dict, wire_order: Wires):
"""Calculate the measurement given a counts histogram dictionary.
Args:
counts (dict): a dictionary matching the format returned by :class:`~.CountsMP`
wire_order (Wires): the wire order used in producing the counts
Note that the input dictionary may only contain states with non-zero entries (``all_outcomes=False``).
"""
[docs]class StateMeasurement(MeasurementProcess):
"""State-based measurement process.
Any class inheriting from ``StateMeasurement`` should define its own ``process_state`` method,
which should have the following arguments:
* state (Sequence[complex]): quantum state with a flat shape. It may also have an
optional batch dimension
* wire_order (Wires): wires determining the subspace that ``state`` acts on; a matrix of
dimension :math:`2^n` acts on a subspace of :math:`n` wires
**Example:**
Let's create a measurement that returns the diagonal of the reduced density matrix.
>>> class MyMeasurement(StateMeasurement):
... def process_state(self, state, wire_order):
... # use the already defined `qml.density_matrix` measurement to compute the
... # reduced density matrix from the given state
... density_matrix = qml.density_matrix(wires=self.wires).process_state(state, wire_order)
... return qml.math.diagonal(qml.math.real(density_matrix))
We can now execute it in a QNode:
>>> dev = qml.device("default.qubit", wires=2)
>>> @qml.qnode(dev)
... def circuit():
... qml.Hadamard(0)
... qml.CNOT([0, 1])
... return MyMeasurement(wires=[0])
>>> circuit()
tensor([0.5, 0.5], requires_grad=True)
"""
[docs] @abstractmethod
def process_state(self, state: Sequence[complex], wire_order: Wires):
"""Process the given quantum state.
Args:
state (Sequence[complex]): quantum state with a flat shape. It may also have an
optional batch dimension
wire_order (Wires): wires determining the subspace that ``state`` acts on; a matrix of
dimension :math:`2^n` acts on a subspace of :math:`n` wires
"""
[docs] def process_density_matrix(self, density_matrix: TensorLike, wire_order: Wires):
"""
Process the given density matrix.
Args:
density_matrix (TensorLike): The density matrix representing the (mixed) quantum state,
which may be single or batched. For a single matrix, the shape should be ``(2^n, 2^n)``
where `n` is the number of wires the matrix acts upon. For batched matrices, the shape
should be ``(batch_size, 2^n, 2^n)``.
wire_order (Wires): The wires determining the subspace that the ``density_matrix`` acts on.
A matrix of dimension :math:`2^n` acts on a subspace of :math:`n` wires. This parameter specifies
the mapping of matrix dimensions to physical qubits, allowing the function to correctly
trace out the subsystems not involved in the measurement or operation.
"""
raise NotImplementedError
[docs]class MeasurementTransform(MeasurementProcess):
"""Measurement process that applies a transform into the given quantum tape. This transform
is carried out inside the gradient black box, thus is not tracked by the gradient transform.
Any class inheriting from ``MeasurementTransform`` should define its own ``process`` method,
which should have the following arguments:
* tape (QuantumTape): quantum tape to transform
* device (pennylane.devices.LegacyDevice): device used to transform the quantum tape
"""
[docs] @abstractmethod
def process(self, tape, device):
"""Process the given quantum tape.
Args:
tape (QuantumTape): quantum tape to transform
device (pennylane.devices.LegacyDevice): device used to transform the quantum tape
"""
_modules/pennylane/measurements/measurements
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