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Add reference.qubit for testing and reference (#6181)
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Created from #5445

[sc-65558]

---------

Co-authored-by: dwierichs <david.wierichs@xanadu.ai>
Co-authored-by: Christina Lee <christina@xanadu.ai>
Co-authored-by: lillian542 <38584660+lillian542@users.noreply.github.com>
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4 people authored and mudit2812 committed Sep 18, 2024
1 parent 6e75f86 commit adb9ab6
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3 changes: 3 additions & 0 deletions doc/releases/changelog-dev.md
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unique representation of the object.
[(#6167)](https://github.com/PennyLaneAI/pennylane/pull/6167)

* A `ReferenceQubit` is introduced for testing purposes and as a reference for future plugin development.
[(#6181)](https://github.com/PennyLaneAI/pennylane/pull/6181)

* The `to_mat` methods for `FermiWord` and `FermiSentence` now optionally return
a sparse matrix.
[(#6173)](https://github.com/PennyLaneAI/pennylane/pull/6173)
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3 changes: 3 additions & 0 deletions pennylane/devices/__init__.py
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_qubit_device
_qutrit_device
null_qubit
reference_qubit
tests
Next generation devices
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DefaultQubit
DefaultTensor
NullQubit
ReferenceQubit
DefaultQutritMixed
LegacyDeviceFacade
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from .default_clifford import DefaultClifford
from .default_tensor import DefaultTensor
from .null_qubit import NullQubit
from .reference_qubit import ReferenceQubit
from .default_qutrit import DefaultQutrit
from .default_qutrit_mixed import DefaultQutritMixed
from ._legacy_device import Device as LegacyDevice
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154 changes: 154 additions & 0 deletions pennylane/devices/reference_qubit.py
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# Copyright 2018-2024 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.
"""
Contains the ReferenceQubit device, a minimal device that can be used for testing
and plugin development purposes.
"""

import numpy as np

import pennylane as qml

from .device_api import Device
from .execution_config import DefaultExecutionConfig
from .modifiers import simulator_tracking, single_tape_support
from .preprocess import decompose, validate_device_wires, validate_measurements


def sample_state(state: np.ndarray, shots: int, seed=None):
"""Generate samples from the provided state and number of shots."""

probs = np.imag(state) ** 2 + np.real(state) ** 2
basis_states = np.arange(len(probs))

num_wires = int(np.log2(len(probs)))

rng = np.random.default_rng(seed)
basis_samples = rng.choice(basis_states, shots, p=probs)

# convert basis state integers to array of booleans
bin_strings = (format(s, f"0{num_wires}b") for s in basis_samples)
return np.array([[int(val) for val in s] for s in bin_strings])


def simulate(tape: qml.tape.QuantumTape, seed=None) -> qml.typing.Result:
"""Simulate a tape and turn it into results.
Args:
tape (.QuantumTape): a representation of a circuit
seed (Any): A seed to use to control the generation of samples.
"""
# 1) create the initial state
state = np.zeros(2 ** len(tape.wires))
state[0] = 1.0

# 2) apply all the operations
for op in tape.operations:
op_mat = op.matrix(wire_order=tape.wires)
state = qml.math.matmul(op_mat, state)

# 3) perform measurements
# note that shots are pulled from the tape, not from the device
if tape.shots:
samples = sample_state(state, shots=tape.shots.total_shots, seed=seed)
# Shot vector support
results = []
for lower, upper in tape.shots.bins():
sub_samples = samples[lower:upper]
results.append(
tuple(mp.process_samples(sub_samples, tape.wires) for mp in tape.measurements)
)
if len(tape.measurements) == 1:
results = [res[0] for res in results]
if not tape.shots.has_partitioned_shots:
results = results[0]
else:
results = tuple(results)
else:
results = tuple(mp.process_state(state, tape.wires) for mp in tape.measurements)
if len(tape.measurements) == 1:
results = results[0]

return results


operations = frozenset({"PauliX", "PauliY", "PauliZ", "Hadamard", "CNOT", "CZ", "RX", "RY", "RZ"})


def supports_operation(op: qml.operation.Operator) -> bool:
"""This function used by preprocessing determines what operations
are natively supported by the device.
While in theory ``simulate`` can support any operation with a matrix, we limit the target
gate set for improved testing and reference purposes.
"""
return getattr(op, "name", None) in operations


@simulator_tracking # update device.tracker with some relevant information
@single_tape_support # add support for device.execute(tape) in addition to device.execute((tape,))
class ReferenceQubit(Device):
"""A slimmed down numpy-based simulator for reference and testing purposes.
Args:
wires (int, Iterable[Number, str]): Number of wires present on the device, or iterable that
contains unique labels for the wires as numbers (i.e., ``[-1, 0, 2]``) or strings
(``['aux', 'q1', 'q2']``). Default ``None`` if not specified. While this device allows
for ``wires`` to be unspecified at construction time, other devices may make this argument
mandatory. Devices can also implement additional restrictions on the possible wires.
shots (int, Sequence[int], Sequence[Union[int, Sequence[int]]]): The default number of shots
to use in executions involving this device. Note that during execution, shots
are pulled from the circuit, not from the device.
seed (Union[str, None, int, array_like[int], SeedSequence, BitGenerator, Generator, jax.random.PRNGKey]): A
seed-like parameter matching that of ``seed`` for ``numpy.random.default_rng``. This is an optional
keyword argument added to follow recommend NumPy best practices. Other devices do not need
this parameter if it does not make sense for them.
"""

name = "reference.qubit"

def __init__(self, wires=None, shots=None, seed=None):
super().__init__(wires=wires, shots=shots)

# seed and rng not necessary for a device, but part of recommended
# numpy practices to use a local random number generator
self._rng = np.random.default_rng(seed)

def preprocess(self, execution_config=DefaultExecutionConfig):

# Here we convert an arbitrary tape into one natively supported by the device
program = qml.transforms.core.TransformProgram()
program.add_transform(validate_device_wires, wires=self.wires, name="reference.qubit")
program.add_transform(qml.defer_measurements)
program.add_transform(qml.transforms.split_non_commuting)
program.add_transform(qml.transforms.diagonalize_measurements)
program.add_transform(
decompose,
stopping_condition=supports_operation,
skip_initial_state_prep=False,
name="reference.qubit",
)
program.add_transform(validate_measurements, name="reference.qubit")
program.add_transform(qml.transforms.broadcast_expand)

# no need to preprocess the config as the device does not support derivatives
return program, execution_config

def execute(self, circuits, execution_config=DefaultExecutionConfig):
for tape in circuits:
assert all(supports_operation(op) for op in tape.operations)
return tuple(simulate(tape, seed=self._rng) for tape in circuits)
1 change: 1 addition & 0 deletions setup.py
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"default.qubit.legacy = pennylane.devices:DefaultQubitLegacy",
"default.gaussian = pennylane.devices:DefaultGaussian",
"default.mixed = pennylane.devices.default_mixed:DefaultMixed",
"reference.qubit = pennylane.devices.reference_qubit:ReferenceQubit",
"null.qubit = pennylane.devices.null_qubit:NullQubit",
"default.qutrit = pennylane.devices.default_qutrit:DefaultQutrit",
"default.clifford = pennylane.devices.default_clifford:DefaultClifford",
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