Source code for pennylane.measurements.classical_shadow
# 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.classical_shadow measurement.
"""
import copy
from collections.abc import Iterable
from typing import Optional, Union, Sequence
from string import ascii_letters as ABC
import numpy as np
import pennylane as qml
from pennylane.operation import Operator
from pennylane.wires import Wires
from .measurements import MeasurementShapeError, MeasurementTransform, Shadow, ShadowExpval
[docs]def shadow_expval(H, k=1, seed=None):
r"""Compute expectation values using classical shadows in a differentiable manner.
The canonical way of computing expectation values is to simply average the expectation values for each local snapshot, :math:`\langle O \rangle = \sum_t \text{tr}(\rho^{(t)}O) / T`.
This corresponds to the case ``k=1``. In the original work, `2002.08953 <https://arxiv.org/abs/2002.08953>`_, it has been proposed to split the ``T`` measurements into ``k`` equal
parts to compute the median of means. For the case of Pauli measurements and Pauli observables, there is no advantage expected from setting ``k>1``.
Args:
H (Union[Iterable[Operator], Operator]): Observable or
iterable of observables to compute the expectation value over.
k (int): Number of equal parts to split the shadow's measurements to compute the median of means. ``k=1`` (default) corresponds to simply taking the mean over all measurements.
seed (Union[None, int]): Seed used to randomly sample Pauli measurements during the
classical shadows protocol. If None, a random seed will be generated. If a tape with
a ``shadow_expval`` measurement is copied, the seed will also be copied.
Different seeds are still generated for different constructed tapes.
Returns:
ShadowExpvalMP: Measurement process instance
.. note::
This measurement uses the measurement :func:`~.pennylane.classical_shadow` and the class :class:`~.pennylane.ClassicalShadow` for post-processing
internally to compute expectation values. In order to compute correct gradients using PennyLane's automatic differentiation,
you need to use this measurement.
**Example**
.. code-block:: python3
H = qml.Hamiltonian([1., 1.], [qml.Z(0) @ qml.Z(1), qml.X(0) @ qml.X(1)])
dev = qml.device("default.qubit", wires=range(2), shots=10000)
@qml.qnode(dev)
def circuit(x, obs):
qml.Hadamard(0)
qml.CNOT((0,1))
qml.RX(x, wires=0)
return qml.shadow_expval(obs)
x = np.array(0.5, requires_grad=True)
We can compute the expectation value of H as well as its gradient in the usual way.
>>> circuit(x, H)
array(1.8774)
>>> qml.grad(circuit)(x, H)
-0.44999999999999984
In ``shadow_expval``, we can pass a list of observables. Note that each qnode execution internally performs one quantum measurement, so be sure
to include all observables that you want to estimate from a single measurement in the same execution.
>>> Hs = [H, qml.X(0), qml.Y(0), qml.Z(0)]
>>> circuit(x, Hs)
array([ 1.881 , -0.0312, -0.0027, -0.0087])
>>> qml.jacobian(circuit)(x, Hs)
array([-0.4518, 0.0174, -0.0216, -0.0063])
"""
seed = seed or np.random.randint(2**30)
return ShadowExpvalMP(H=H, seed=seed, k=k)
[docs]def classical_shadow(wires, seed=None):
"""
The classical shadow measurement protocol.
The protocol is described in detail in the paper `Predicting Many Properties of a Quantum System from Very Few Measurements <https://arxiv.org/abs/2002.08953>`_.
This measurement process returns the randomized Pauli measurements (the ``recipes``)
that are performed for each qubit and snapshot as an integer:
- 0 for Pauli X,
- 1 for Pauli Y, and
- 2 for Pauli Z.
It also returns the measurement results (the ``bits``); 0 if the 1 eigenvalue
is sampled, and 1 if the -1 eigenvalue is sampled.
The device shots are used to specify the number of snapshots. If ``T`` is the number
of shots and ``n`` is the number of qubits, then both the measured bits and the
Pauli measurements have shape ``(T, n)``.
Args:
wires (Sequence[int]): the wires to perform Pauli measurements on
seed (Union[None, int]): Seed used to randomly sample Pauli measurements during the
classical shadows protocol. If None, a random seed will be generated. If a tape with
a ``classical_shadow`` measurement is copied, the seed will also be copied.
Different seeds are still generated for different constructed tapes.
Returns:
ClassicalShadowMP: measurement process instance
**Example**
Consider the following QNode that prepares a Bell state and performs a classical
shadow measurement:
.. code-block:: python3
dev = qml.device("default.qubit", wires=2, shots=5)
@qml.qnode(dev)
def circuit():
qml.Hadamard(wires=0)
qml.CNOT(wires=[0, 1])
return qml.classical_shadow(wires=[0, 1])
Executing this QNode produces the sampled bits and the Pauli measurements used:
>>> bits, recipes = circuit()
>>> bits
tensor([[0, 0],
[1, 0],
[1, 0],
[0, 0],
[0, 1]], dtype=uint8, requires_grad=True)
>>> recipes
tensor([[2, 2],
[0, 2],
[1, 0],
[0, 2],
[0, 2]], dtype=uint8, requires_grad=True)
.. details::
:title: Usage Details
Consider again the QNode in the above example. Since the Pauli observables are
randomly sampled, executing this QNode again would produce different bits and Pauli recipes:
>>> bits, recipes = circuit()
>>> bits
tensor([[0, 1],
[0, 1],
[0, 0],
[0, 1],
[1, 1]], dtype=uint8, requires_grad=True)
>>> recipes
tensor([[1, 0],
[2, 1],
[2, 2],
[1, 0],
[0, 0]], dtype=uint8, requires_grad=True)
To use the same Pauli recipes for different executions, the :class:`~.tape.QuantumTape`
interface should be used instead:
.. code-block:: python3
dev = qml.device("default.qubit", wires=2, shots=5)
ops = [qml.Hadamard(wires=0), qml.CNOT(wires=(0,1))]
measurements = [qml.classical_shadow(wires=(0,1))]
tape = qml.tape.QuantumTape(ops, measurements, shots=5)
>>> bits1, recipes1 = qml.execute([tape], device=dev, gradient_fn=None)[0]
>>> bits2, recipes2 = qml.execute([tape], device=dev, gradient_fn=None)[0]
>>> np.all(recipes1 == recipes2)
True
>>> np.all(bits1 == bits2)
False
If using different Pauli recipes is desired for the :class:`~.tape.QuantumTape` interface,
different seeds should be used for the classical shadow:
.. code-block:: python3
dev = qml.device("default.qubit", wires=2, shots=5)
measurements1 = [qml.classical_shadow(wires=(0,1), seed=10)]
tape1 = qml.tape.QuantumTape(ops, measurements1, shots=5)
measurements2 = [qml.classical_shadow(wires=(0,1), seed=15)]
tape2 = qml.tape.QuantumTape(ops, measurements2, shots=5)
>>> bits1, recipes1 = qml.execute([tape1], device=dev, gradient_fn=None)[0]
>>> bits2, recipes2 = qml.execute([tape2], device=dev, gradient_fn=None)[0]
>>> np.all(recipes1 == recipes2)
False
>>> np.all(bits1 == bits2)
False
"""
wires = Wires(wires)
seed = seed or np.random.randint(2**30)
return ClassicalShadowMP(wires=wires, seed=seed)
[docs]class ClassicalShadowMP(MeasurementTransform):
"""Represents a classical shadow measurement process occurring at the end of a
quantum variational circuit.
Please refer to :func:`classical_shadow` for detailed documentation.
Args:
wires (.Wires): The wires the measurement process applies to.
seed (Union[int, None]): The seed used to generate the random measurements
id (str): custom label given to a measurement instance, can be useful for some applications
where the instance has to be identified
"""
def __init__(
self, wires: Optional[Wires] = None, seed: Optional[int] = None, id: Optional[str] = None
):
self.seed = seed
super().__init__(wires=wires, id=id)
def _flatten(self):
metadata = (("wires", self.wires), ("seed", self.seed))
return (None, None), metadata
@property
def hash(self):
"""int: returns an integer hash uniquely representing the measurement process"""
fingerprint = (
self.__class__.__name__,
self.seed,
tuple(self.wires.tolist()),
)
return hash(fingerprint)
[docs] def process(self, tape, device):
"""
Returns the measured bits and recipes in the classical shadow protocol.
The protocol is described in detail in the `classical shadows paper <https://arxiv.org/abs/2002.08953>`_.
This measurement process returns the randomized Pauli measurements (the ``recipes``)
that are performed for each qubit and snapshot as an integer:
- 0 for Pauli X,
- 1 for Pauli Y, and
- 2 for Pauli Z.
It also returns the measurement results (the ``bits``); 0 if the 1 eigenvalue
is sampled, and 1 if the -1 eigenvalue is sampled.
The device shots are used to specify the number of snapshots. If ``T`` is the number
of shots and ``n`` is the number of qubits, then both the measured bits and the
Pauli measurements have shape ``(T, n)``.
This implementation is device-agnostic and works by executing single-shot
quantum tapes containing randomized Pauli observables. Devices should override this
if they can offer cleaner or faster implementations.
.. seealso:: :func:`~pennylane.classical_shadow`
Args:
tape (QuantumTape): the quantum tape to be processed
device (pennylane.Device): the device used to process the quantum tape
Returns:
tensor_like[int]: A tensor with shape ``(2, T, n)``, where the first row represents
the measured bits and the second represents the recipes used.
"""
wires = self.wires
n_snapshots = device.shots
seed = self.seed
with qml.workflow.set_shots(device, shots=1):
# slow implementation but works for all devices
n_qubits = len(wires)
mapped_wires = np.array(device.map_wires(wires))
# seed the random measurement generation so that recipes
# are the same for different executions with the same seed
rng = np.random.RandomState(seed)
recipes = rng.randint(0, 3, size=(n_snapshots, n_qubits))
obs_list = [qml.X, qml.Y, qml.Z]
outcomes = np.zeros((n_snapshots, n_qubits))
for t in range(n_snapshots):
# compute rotations for the Pauli measurements
rotations = [
rot
for wire_idx, wire in enumerate(wires)
for rot in obs_list[recipes[t][wire_idx]].compute_diagonalizing_gates(
wires=wire
)
]
device.reset()
device.apply(tape.operations, rotations=tape.diagonalizing_gates + rotations)
outcomes[t] = device.generate_samples()[0][mapped_wires]
return qml.math.cast(qml.math.stack([outcomes, recipes]), dtype=np.int8)
[docs] def process_state_with_shots(
self, state: Sequence[complex], wire_order: Wires, shots: int, rng=None
):
"""Process the given quantum state with the given number of shots
Args:
state (Sequence[complex]): quantum state vector given as a rank-N tensor, where
each dim has size 2 and N is the number of wires.
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
shots (int): The number of shots
rng (Union[None, int, array_like[int], SeedSequence, BitGenerator, Generator]): A
seed-like parameter matching that of ``seed`` for ``numpy.random.default_rng``.
If no value is provided, a default RNG will be used. The random measurement outcomes
in the form of bits will be generated from this argument, while the random recipes will be
created from the ``seed`` argument provided to ``.ClassicalShadowsMP``.
Returns:
tensor_like[int]: A tensor with shape ``(2, T, n)``, where the first row represents
the measured bits and the second represents the recipes used.
"""
wire_map = {w: i for i, w in enumerate(wire_order)}
mapped_wires = [wire_map[w] for w in self.wires]
n_qubits = len(mapped_wires)
num_dev_qubits = len(state.shape)
# seed the random measurement generation so that recipes
# are the same for different executions with the same seed
recipe_rng = np.random.RandomState(self.seed)
recipes = recipe_rng.randint(0, 3, size=(shots, n_qubits))
bit_rng = np.random.default_rng(rng)
obs_list = np.stack(
[
qml.X.compute_matrix(),
qml.Y.compute_matrix(),
qml.Z.compute_matrix(),
]
)
# the diagonalizing matrices corresponding to the Pauli observables above
diag_list = np.stack(
[
qml.Hadamard.compute_matrix(),
qml.Hadamard.compute_matrix() @ qml.RZ.compute_matrix(-np.pi / 2),
qml.Identity.compute_matrix(),
]
)
obs = obs_list[recipes]
diagonalizers = diag_list[recipes]
# There's a significant speedup if we use the following iterative
# process to perform the randomized Pauli measurements:
# 1. Randomly generate Pauli observables for all snapshots for
# a single qubit (e.g. the first qubit).
# 2. Compute the expectation of each Pauli observable on the first
# qubit by tracing out all other qubits.
# 3. Sample the first qubit based on each Pauli expectation.
# 4. For all snapshots, determine the collapsed state of the remaining
# qubits based on the sample result.
# 4. Repeat iteratively until no qubits are remaining.
#
# Observe that after the first iteration, the second qubit will become the
# "first" qubit in the process. The advantage to this approach as opposed to
# simulataneously computing the Pauli expectations for each qubit is that
# the partial traces are computed over iteratively smaller subsystems, leading
# to a significant speed-up.
# transpose the state so that the measured wires appear first
unmeasured_wires = [i for i in range(num_dev_qubits) if i not in mapped_wires]
transposed_state = np.transpose(state, axes=mapped_wires + unmeasured_wires)
outcomes = np.zeros((shots, n_qubits))
stacked_state = np.repeat(transposed_state[np.newaxis, ...], shots, axis=0)
for active_qubit in range(n_qubits):
# stacked_state loses a dimension each loop
# trace out every qubit except the first
num_remaining_qubits = num_dev_qubits - active_qubit
conj_state_first_qubit = ABC[num_remaining_qubits]
stacked_dim = ABC[num_remaining_qubits + 1]
state_str = f"{stacked_dim}{ABC[:num_remaining_qubits]}"
conj_state_str = f"{stacked_dim}{conj_state_first_qubit}{ABC[1:num_remaining_qubits]}"
target_str = f"{stacked_dim}a{conj_state_first_qubit}"
first_qubit_state = np.einsum(
f"{state_str},{conj_state_str}->{target_str}",
stacked_state,
np.conj(stacked_state),
)
# sample the observables on the first qubit
probs = (np.einsum("abc,acb->a", first_qubit_state, obs[:, active_qubit]) + 1) / 2
samples = bit_rng.random(size=probs.shape) > probs
outcomes[:, active_qubit] = samples
# collapse the state of the remaining qubits; the next qubit in line
# becomes the first qubit for the next iteration
rotated_state = np.einsum(
"ab...,acb->ac...", stacked_state, diagonalizers[:, active_qubit]
)
stacked_state = rotated_state[np.arange(shots), samples.astype(np.int8)]
# re-normalize the collapsed state
sum_indices = tuple(range(1, num_remaining_qubits))
state_squared = np.abs(stacked_state) ** 2
norms = np.sqrt(np.sum(state_squared, sum_indices, keepdims=True))
stacked_state /= norms
return np.stack([outcomes, recipes]).astype(np.int8)
@property
def samples_computational_basis(self):
return False
@property
def numeric_type(self):
return int
@property
def return_type(self):
return Shadow
[docs] def shape(self, device, shots): # pylint: disable=unused-argument
# otherwise, the return type requires a device
if not shots:
raise MeasurementShapeError(
"Shots must be specified to obtain the shape of a classical "
"shadow measurement process."
)
# the first entry of the tensor represents the measured bits,
# and the second indicate the indices of the unitaries used
return (2, shots.total_shots, len(self.wires))
def __copy__(self):
return self.__class__(
seed=self.seed,
wires=self._wires,
)
[docs]class ShadowExpvalMP(MeasurementTransform):
"""Measures the expectation value of an operator using the classical shadow measurement process.
Please refer to :func:`shadow_expval` for detailed documentation.
Args:
H (Operator, Sequence[Operator]): Operator or list of Operators to compute the expectation value over.
seed (Union[int, None]): The seed used to generate the random measurements
k (int): Number of equal parts to split the shadow's measurements to compute the median of means.
``k=1`` corresponds to simply taking the mean over all measurements.
id (str): custom label given to a measurement instance, can be useful for some applications
where the instance has to be identified
"""
def _flatten(self):
metadata = (
("seed", self.seed),
("k", self.k),
)
return (self.H,), metadata
@classmethod
def _unflatten(cls, data, metadata):
return cls(data[0], **dict(metadata))
def __init__(
self,
H: Union[Operator, Sequence],
seed: Optional[int] = None,
k: int = 1,
id: Optional[str] = None,
):
self.seed = seed
self.H = H
self.k = k
super().__init__(id=id)
[docs] def process(self, tape, device):
bits, recipes = qml.classical_shadow(wires=self.wires, seed=self.seed).process(tape, device)
shadow = qml.shadows.ClassicalShadow(bits, recipes, wire_map=self.wires.tolist())
return shadow.expval(self.H, self.k)
[docs] def process_state_with_shots(
self, state: Sequence[complex], wire_order: Wires, shots: int, rng=None
):
"""Process the given quantum state with the given number of shots
Args:
state (Sequence[complex]): quantum state
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
shots (int): The number of shots
rng (Union[None, int, array_like[int], SeedSequence, BitGenerator, Generator]): A
seed-like parameter matching that of ``seed`` for ``numpy.random.default_rng``.
If no value is provided, a default RNG will be used.
Returns:
float: The estimate of the expectation value.
"""
bits, recipes = qml.classical_shadow(
wires=self.wires, seed=self.seed
).process_state_with_shots(state, wire_order, shots, rng=rng)
shadow = qml.shadows.ClassicalShadow(bits, recipes, wire_map=self.wires.tolist())
return shadow.expval(self.H, self.k)
@property
def samples_computational_basis(self):
return False
@property
def numeric_type(self):
return float
@property
def return_type(self):
return ShadowExpval
@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 isinstance(self.H, Iterable):
return Wires.all_wires([h.wires for h in self.H])
return self.H.wires
[docs] def queue(self, context=qml.QueuingManager):
"""Append the measurement process to an annotated queue, making sure
the observable is not queued"""
Hs = self.H if isinstance(self.H, Iterable) else [self.H]
for H in Hs:
context.remove(H)
context.append(self)
return self
def __copy__(self):
H_copy = (
[copy.copy(H) for H in self.H] if isinstance(self.H, Iterable) else copy.copy(self.H)
)
return self.__class__(
H=H_copy,
k=self.k,
seed=self.seed,
)
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