Source code for pennylane.devices.execution_config
# Copyright 2018-2023 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 :class:`ExecutionConfig` data class.
"""
from dataclasses import dataclass, field
from typing import Optional, Union
from pennylane.math import Interface, get_canonical_interface_name
from pennylane.transforms.core import TransformDispatcher
[docs]@dataclass
class MCMConfig:
"""A class to store mid-circuit measurement configurations."""
mcm_method: Optional[str] = None
"""The mid-circuit measurement strategy to use. Use ``"deferred"`` for the deferred
measurements principle and ``"one-shot"`` if using finite shots to execute the circuit for
each shot separately. Any other value will be passed to the device, and the device is
expected to handle mid-circuit measurements using the requested method. If not specified,
the device will decide which method to use."""
postselect_mode: Optional[str] = None
"""How postselection is handled with finite-shots. If ``"hw-like"``, invalid shots will be
discarded and only results for valid shots will be returned. In this case, fewer samples
may be returned than the original number of shots. If ``"fill-shots"``, the returned samples
will be of the same size as the original number of shots. If not specified, the device will
decide which mode to use. Note that internally ``"pad-invalid-samples"`` is used internally
instead of ``"hw-like"`` when using jax/catalyst"""
def __post_init__(self):
"""Validate the configured mid-circuit measurement options."""
if self.postselect_mode not in ("hw-like", "fill-shots", "pad-invalid-samples", None):
raise ValueError(f"Invalid postselection mode '{self.postselect_mode}'.")
# pylint: disable=too-many-instance-attributes
[docs]@dataclass
class ExecutionConfig:
"""
A class to configure the execution of a quantum circuit on a device.
See the Attributes section to learn more about the various configurable options.
"""
grad_on_execution: Optional[bool] = None
"""Whether or not to compute the gradient at the same time as the execution.
If ``None``, then the device or execution pipeline can decide which one is most efficient for the situation.
"""
use_device_gradient: Optional[bool] = None
"""Whether or not to compute the gradient on the device.
``None`` indicates to use the device if possible, but to fall back to pennylane behaviour if it isn't.
True indicates a request to either use the device gradient or fail.
"""
use_device_jacobian_product: Optional[bool] = None
"""Whether or not to use the device provided vjp or jvp to compute gradients.
``None`` indicates to use the device if possible, but to fall back to the device Jacobian
or PennyLane behaviour if it isn't.
``True`` indicates to either use the device Jacobian products or fail.
"""
gradient_method: Optional[Union[str, TransformDispatcher]] = None
"""The method used to compute the gradient of the quantum circuit being executed"""
gradient_keyword_arguments: Optional[dict] = None
"""Arguments used to control a gradient transform"""
device_options: Optional[dict] = None
"""Various options for the device executing a quantum circuit"""
interface: Interface = Interface.NUMPY
"""The machine learning framework to use"""
derivative_order: int = 1
"""The derivative order to compute while evaluating a gradient"""
mcm_config: MCMConfig = field(default_factory=MCMConfig)
"""Configuration options for handling mid-circuit measurements"""
convert_to_numpy: bool = True
"""Whether or not to convert parameters to numpy before execution.
If ``False`` and using the jax-jit, no pure callback will occur and the device
execution itself will be jitted.
"""
def __post_init__(self):
"""
Validate the configured execution options.
Note that this hook is automatically called after init via the dataclass integration.
"""
self.interface = get_canonical_interface_name(self.interface)
if self.grad_on_execution not in {True, False, None}:
raise ValueError(
f"grad_on_execution must be True, False, or None. Got {self.grad_on_execution} instead."
)
if self.device_options is None:
self.device_options = {}
if self.gradient_keyword_arguments is None:
self.gradient_keyword_arguments = {}
if not (
isinstance(self.gradient_method, (str, TransformDispatcher))
or self.gradient_method is None
):
raise ValueError(
f"Differentiation method {self.gradient_method} must be a str, TransformDispatcher, or None. Got {type(self.gradient_method)} instead."
)
if isinstance(self.mcm_config, dict):
self.mcm_config = MCMConfig(**self.mcm_config) # pylint: disable=not-a-mapping
elif not isinstance(self.mcm_config, MCMConfig):
raise ValueError(f"Got invalid type {type(self.mcm_config)} for 'mcm_config'")
DefaultExecutionConfig = ExecutionConfig()
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