# 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.
r"""
This module contains the LightningQubit class, a PennyLane simulator device that
interfaces with C++ for fast linear algebra calculations.
"""
import os
import sys
from dataclasses import replace
from functools import reduce
from pathlib import Path
from typing import List, Optional, Sequence, Union
from warnings import warn
import numpy as np
import pennylane as qml
from pennylane.devices import DefaultExecutionConfig, ExecutionConfig
from pennylane.devices.capabilities import OperatorProperties
from pennylane.devices.modifiers import simulator_tracking, single_tape_support
from pennylane.devices.preprocess import (
decompose,
mid_circuit_measurements,
no_sampling,
validate_adjoint_trainable_params,
validate_device_wires,
validate_measurements,
validate_observables,
)
from pennylane.measurements import MidMeasureMP
from pennylane.operation import DecompositionUndefinedError, Operator
from pennylane.ops import Conditional, PauliRot, Prod, SProd, Sum
from pennylane.tape import QuantumScript
from pennylane.transforms.core import TransformProgram
from pennylane.typing import Result
from pennylane_lightning.core.lightning_base import (
LightningBase,
QuantumTape_or_Batch,
Result_or_ResultBatch,
)
try:
from pennylane_lightning.lightning_qubit_ops import backend_info
LQ_CPP_BINARY_AVAILABLE = True
except ImportError as ex:
warn(str(ex), UserWarning)
LQ_CPP_BINARY_AVAILABLE = False
from ._adjoint_jacobian import LightningAdjointJacobian
from ._measurements import LightningMeasurements
from ._state_vector import LightningStateVector
_to_matrix_ops = {
"BlockEncode": OperatorProperties(controllable=True),
"DiagonalQubitUnitary": OperatorProperties(),
"ECR": OperatorProperties(),
"ISWAP": OperatorProperties(),
"OrbitalRotation": OperatorProperties(),
"PSWAP": OperatorProperties(),
"QubitCarry": OperatorProperties(),
"QubitSum": OperatorProperties(),
"SISWAP": OperatorProperties(),
"SQISW": OperatorProperties(),
}
def stopping_condition(op: Operator) -> bool:
"""A function that determines whether or not an operation is supported by ``lightning.qubit``."""
# As ControlledQubitUnitary == C(QubitUnitrary),
# it can be removed from `_operations` to keep
# consistency with `lightning_qubit.toml`
if isinstance(op, qml.ControlledQubitUnitary):
return True
if isinstance(op, qml.PauliRot):
word = op._hyperparameters["pauli_word"] # pylint: disable=protected-access
# decomposes to IsingXX, etc. for n <= 2
return reduce(lambda x, y: x + (y != "I"), word, 0) > 2
return _supports_operation(op.name)
def stopping_condition_shots(op: Operator) -> bool:
"""A function that determines whether or not an operation is supported by ``lightning.qubit``
with finite shots."""
return stopping_condition(op) or isinstance(op, (MidMeasureMP, qml.ops.op_math.Conditional))
def accepted_observables(obs: Operator) -> bool:
"""A function that determines whether or not an observable is supported by ``lightning.qubit``."""
return _supports_observable(obs.name)
def adjoint_observables(obs: Operator) -> bool:
"""A function that determines whether or not an observable is supported by ``lightning.qubit``
when using the adjoint differentiation method."""
if isinstance(obs, qml.Projector):
return False
if isinstance(obs, SProd):
return adjoint_observables(obs.base)
if isinstance(obs, (Sum, Prod)):
return all(adjoint_observables(o) for o in obs)
return _supports_observable(obs.name)
def adjoint_measurements(mp: qml.measurements.MeasurementProcess) -> bool:
"""Specifies whether or not an observable is compatible with adjoint differentiation on DefaultQubit."""
return isinstance(mp, qml.measurements.ExpectationMP)
def _supports_adjoint(circuit):
if circuit is None:
return True
prog = TransformProgram()
_add_adjoint_transforms(prog)
try:
prog((circuit,))
except (DecompositionUndefinedError, qml.DeviceError, AttributeError):
return False
return True
def _adjoint_ops(op: qml.operation.Operator) -> bool:
"""Specify whether or not an Operator is supported by adjoint differentiation."""
return not isinstance(op, (Conditional, MidMeasureMP, PauliRot)) and (
not qml.operation.is_trainable(op) or (op.num_params == 1 and op.has_generator)
)
def _add_adjoint_transforms(program: TransformProgram) -> None:
"""Private helper function for ``preprocess`` that adds the transforms specific
for adjoint differentiation.
Args:
program (TransformProgram): where we will add the adjoint differentiation transforms
Side Effects:
Adds transforms to the input program.
"""
name = "adjoint + lightning.qubit"
program.add_transform(no_sampling, name=name)
program.add_transform(
decompose,
stopping_condition=_adjoint_ops,
stopping_condition_shots=stopping_condition_shots,
name=name,
skip_initial_state_prep=False,
)
program.add_transform(validate_observables, accepted_observables, name=name)
program.add_transform(
validate_measurements, analytic_measurements=adjoint_measurements, name=name
)
program.add_transform(qml.transforms.broadcast_expand)
program.add_transform(validate_adjoint_trainable_params)
[docs]@simulator_tracking
@single_tape_support
class LightningQubit(LightningBase):
"""PennyLane Lightning Qubit device.
A device that interfaces with C++ to perform fast linear algebra calculations.
Use of this device requires pre-built binaries or compilation from source. Check out the
:doc:`/lightning_qubit/installation` guide for more details.
Args:
wires (Optional[int, list]): the number of wires to initialize the device with. Defaults to ``None`` if not specified, and the device will allocate the number of wires depending on the circuit to execute.
c_dtype: Datatypes for statevector representation. Must be one of
``np.complex64`` or ``np.complex128``.
shots (int): How many times the circuit should be evaluated (or sampled) to estimate
the expectation values. Defaults to ``None`` if not specified. Setting
to ``None`` results in computing statistics like expectation values and
variances analytically.
seed (Union[str, None, int, array_like[int], SeedSequence, BitGenerator, Generator]): A
seed-like parameter matching that of ``seed`` for ``numpy.random.default_rng``, or
a request to seed from numpy's global random number generator.
The default, ``seed="global"`` pulls a seed from NumPy's global generator. ``seed=None``
will pull a seed from the OS entropy.
mcmc (bool): Determine whether to use the approximate Markov Chain Monte Carlo
sampling method when generating samples.
kernel_name (str): name of transition MCMC kernel. The current version supports
two kernels: ``"Local"`` and ``"NonZeroRandom"``.
The local kernel conducts a bit-flip local transition between states.
The local kernel generates a random qubit site and then generates a random
number to determine the new bit at that qubit site. The ``"NonZeroRandom"`` kernel
randomly transits between states that have nonzero probability.
num_burnin (int): number of MCMC steps that will be dropped. Increasing this value will
result in a closer approximation but increased runtime.
batch_obs (bool): Determine whether we process observables in parallel when
computing the jacobian. This value is only relevant when the lightning
qubit is built with OpenMP.
"""
# pylint: disable=too-many-instance-attributes
# General device options
_device_options = ("rng", "c_dtype", "batch_obs", "mcmc", "kernel_name", "num_burnin")
# Device specific options
_CPP_BINARY_AVAILABLE = LQ_CPP_BINARY_AVAILABLE
_backend_info = backend_info if LQ_CPP_BINARY_AVAILABLE else None
# This configuration file declares the device capabilities
config_filepath = Path(__file__).parent / "lightning_qubit.toml"
# TODO: This is to communicate to Catalyst in qjit-compiled workflows that these operations
# should be converted to QubitUnitary instead of their original decompositions. Remove
# this when customizable multiple decomposition pathways are implemented
_to_matrix_ops = _to_matrix_ops
def __init__( # pylint: disable=too-many-arguments
self,
wires: Union[int, List] = None,
*,
c_dtype: Union[np.complex128, np.complex64] = np.complex128,
shots: Union[int, List] = None,
batch_obs: bool = False,
# Markov Chain Monte Carlo (MCMC) sampling method arguments
seed: Union[str, int] = "global",
mcmc: bool = False,
kernel_name: str = "Local",
num_burnin: int = 100,
):
if not self._CPP_BINARY_AVAILABLE:
raise ImportError(
"Pre-compiled binaries for lightning.qubit are not available. "
"To manually compile from source, follow the instructions at "
"https://docs.pennylane.ai/projects/lightning/en/stable/dev/installation.html."
)
super().__init__(
wires=wires,
c_dtype=c_dtype,
shots=shots,
batch_obs=batch_obs,
)
# Set the attributes to call the Lightning classes
self._set_lightning_classes()
# Markov Chain Monte Carlo (MCMC) sampling method specific options
# TODO: Investigate usefulness of creating numpy random generator
seed = np.random.randint(0, high=10000000) if seed == "global" else seed
self._rng = np.random.default_rng(seed)
self._mcmc = mcmc
if self._mcmc:
if kernel_name not in [
"Local",
"NonZeroRandom",
]:
raise NotImplementedError(
f"The {kernel_name} is not supported and currently "
"only 'Local' and 'NonZeroRandom' kernels are supported."
)
shots = shots if isinstance(shots, Sequence) else [shots]
if any(num_burnin >= s for s in shots):
raise ValueError("Shots should be greater than num_burnin.")
self._kernel_name = kernel_name
self._num_burnin = num_burnin
else:
self._kernel_name = None
self._num_burnin = 0
self.device_kwargs = {
"mcmc": self._mcmc,
"num_burnin": self._num_burnin,
"kernel_name": self._kernel_name,
}
self._statevector = None
self._sv_init_kwargs = {}
@property
def name(self):
"""The name of the device."""
return "lightning.qubit"
def _set_lightning_classes(self):
"""Load the LightningStateVector, LightningMeasurements, LightningAdjointJacobian as class attribute"""
self.LightningStateVector = LightningStateVector
self.LightningMeasurements = LightningMeasurements
self.LightningAdjointJacobian = LightningAdjointJacobian
def _setup_execution_config(self, config):
"""
Update the execution config with choices for how the device should be used and the device options.
"""
updated_values = {}
if config.gradient_method == "best":
updated_values["gradient_method"] = "adjoint"
if config.use_device_gradient is None:
updated_values["use_device_gradient"] = config.gradient_method in ("best", "adjoint")
if (
config.use_device_gradient
or updated_values.get("use_device_gradient", False)
and config.grad_on_execution is None
):
updated_values["grad_on_execution"] = True
new_device_options = dict(config.device_options)
for option in self._device_options:
if option not in new_device_options:
new_device_options[option] = getattr(self, f"_{option}", None)
return replace(config, **updated_values, device_options=new_device_options)
[docs] def preprocess(self, execution_config: ExecutionConfig = DefaultExecutionConfig):
"""This function defines the device transform program to be applied and an updated device configuration.
Args:
execution_config (Union[ExecutionConfig, Sequence[ExecutionConfig]]): A data structure describing the
parameters needed to fully describe the execution.
Returns:
TransformProgram, ExecutionConfig: A transform program that when called returns :class:`~.QuantumTape`'s that the
device can natively execute as well as a postprocessing function to be called after execution, and a configuration
with unset specifications filled in.
This device:
* Supports any qubit operations that provide a matrix
* Currently does not support finite shots
* Currently does not intrinsically support parameter broadcasting
"""
exec_config = self._setup_execution_config(execution_config)
program = TransformProgram()
program.add_transform(validate_measurements, name=self.name)
program.add_transform(validate_observables, accepted_observables, name=self.name)
program.add_transform(validate_device_wires, self.wires, name=self.name)
program.add_transform(
mid_circuit_measurements, device=self, mcm_config=exec_config.mcm_config
)
program.add_transform(
decompose,
stopping_condition=stopping_condition,
stopping_condition_shots=stopping_condition_shots,
skip_initial_state_prep=True,
name=self.name,
)
program.add_transform(qml.transforms.broadcast_expand)
if exec_config.gradient_method == "adjoint":
_add_adjoint_transforms(program)
return program, exec_config
# pylint: disable=unused-argument
[docs] def execute(
self,
circuits: QuantumTape_or_Batch,
execution_config: ExecutionConfig = DefaultExecutionConfig,
) -> Result_or_ResultBatch:
"""Execute a circuit or a batch of circuits and turn it into results.
Args:
circuits (Union[QuantumTape, Sequence[QuantumTape]]): the quantum circuits to be executed
execution_config (ExecutionConfig): a datastructure with additional information required for execution
Returns:
TensorLike, tuple[TensorLike], tuple[tuple[TensorLike]]: A numeric result of the computation.
"""
mcmc = {
"mcmc": self._mcmc,
"kernel_name": self._kernel_name,
"num_burnin": self._num_burnin,
}
results = []
for circuit in circuits:
if self._wire_map is not None:
[circuit], _ = qml.map_wires(circuit, self._wire_map)
results.append(
self.simulate(
self.dynamic_wires_from_circuit(circuit),
self._statevector,
mcmc=mcmc,
postselect_mode=execution_config.mcm_config.postselect_mode,
)
)
return tuple(results)
[docs] def supports_derivatives(
self,
execution_config: Optional[ExecutionConfig] = None,
circuit: Optional[qml.tape.QuantumTape] = None,
) -> bool:
"""Check whether or not derivatives are available for a given configuration and circuit.
``LightningQubit`` supports adjoint differentiation with analytic results.
Args:
execution_config (ExecutionConfig): The configuration of the desired derivative calculation
circuit (QuantumTape): An optional circuit to check derivatives support for.
Returns:
Bool: Whether or not a derivative can be calculated provided the given information
"""
if execution_config is None and circuit is None:
return True
if execution_config.gradient_method not in {"adjoint", "best"}:
return False
if circuit is None:
return True
return _supports_adjoint(circuit=circuit)
[docs] def simulate(
self,
circuit: QuantumScript,
state: LightningStateVector,
mcmc: dict = None,
postselect_mode: str = None,
) -> Result:
"""Simulate a single quantum script.
Args:
circuit (QuantumTape): The single circuit to simulate
state (LightningStateVector): handle to Lightning state vector
mcmc (dict): Dictionary containing the Markov Chain Monte Carlo
parameters: mcmc, kernel_name, num_burnin. Descriptions of
these fields are found in :class:`~.LightningQubit`.
postselect_mode (str): Configuration for handling shots with mid-circuit measurement
postselection. Use ``"hw-like"`` to discard invalid shots and ``"fill-shots"`` to
keep the same number of shots. Default is ``None``.
Returns:
Tuple[TensorLike]: The results of the simulation
Note that this function can return measurements for non-commuting observables simultaneously.
"""
if mcmc is None:
mcmc = {}
if circuit.shots and (any(isinstance(op, MidMeasureMP) for op in circuit.operations)):
results = []
aux_circ = qml.tape.QuantumScript(
circuit.operations,
circuit.measurements,
shots=[1],
trainable_params=circuit.trainable_params,
)
for _ in range(circuit.shots.total_shots):
state.reset_state()
mid_measurements = {}
final_state = state.get_final_state(
aux_circ, mid_measurements=mid_measurements, postselect_mode=postselect_mode
)
results.append(
LightningMeasurements(final_state, **mcmc).measure_final_state(
aux_circ, mid_measurements=mid_measurements
)
)
return tuple(results)
final_state = state.get_final_state(circuit)
return self.LightningMeasurements(final_state, **mcmc).measure_final_state(circuit)
[docs] @staticmethod
def get_c_interface():
"""Returns a tuple consisting of the device name, and
the location to the shared object with the C/C++ device implementation.
"""
# The shared object file extension varies depending on the underlying operating system
file_extension = ""
OS = sys.platform
if OS == "linux":
file_extension = ".so"
elif OS == "darwin":
file_extension = ".dylib"
else:
raise RuntimeError(
f"'LightningSimulator' shared library not available for '{OS}' platform"
) # pragma: no cover
lib_name = "liblightning_qubit_catalyst" + file_extension
package_root = Path(__file__).parent
# The absolute path of the plugin shared object varies according to the installation mode.
# Wheel mode:
# Fixed location at the root of the project
wheel_mode_location = package_root.parent / lib_name
if wheel_mode_location.is_file():
return "LightningSimulator", wheel_mode_location.as_posix()
# Editable mode:
# The build directory contains a folder which varies according to the platform:
# lib.<system>-<architecture>-<python-id>"
# To avoid mismatching the folder name, we search for the shared object instead.
# TODO: locate where the naming convention of the folder is decided and replicate it here.
editable_mode_path = package_root.parent.parent / "build_lightning_qubit"
for path, _, files in os.walk(editable_mode_path):
if lib_name in files:
lib_location = (Path(path) / lib_name).as_posix()
return "LightningSimulator", lib_location
raise RuntimeError("'LightningSimulator' shared library not found") # pragma: no cover
_supports_operation = LightningQubit.capabilities.supports_operation
_supports_observable = LightningQubit.capabilities.supports_observable