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
from typing import Optional

from pennylane.workflow import SUPPORTED_INTERFACES
from pennylane.gradients import SUPPORTED_GRADIENT_KWARGS

SUPPORTED_GRADIENT_METHODS = [
    "best",
    "parameter-shift",
    "backprop",
    "finite-diff",
    "device",
    "adjoint",
    "gradient-transform",
]


# pylint: disable=too-many-instance-attributes
@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 behavior 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[str] = 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: Optional[str] = None
    """The machine learning framework to use"""

    derivative_order: int = 1
    """The derivative order to compute while evaluating a gradient"""

    def __post_init__(self):
        """
        Validate the configured execution options.

        Note that this hook is automatically called after init via the dataclass integration.
        """
        if self.interface not in SUPPORTED_INTERFACES:
            raise ValueError(
                f"Unknown interface. interface must be in {SUPPORTED_INTERFACES}, got {self.interface} instead."
            )

        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.gradient_method is not None
            and self.gradient_method not in SUPPORTED_GRADIENT_METHODS
        ):
            raise ValueError(
                f"gradient_method must be in {SUPPORTED_GRADIENT_METHODS}, got {self.gradient_method} instead."
            )

        if self.device_options is None:
            self.device_options = {}

        if self.gradient_keyword_arguments is None:
            self.gradient_keyword_arguments = {}

        if any(arg not in SUPPORTED_GRADIENT_KWARGS for arg in self.gradient_keyword_arguments):
            raise ValueError(
                f"All gradient_keyword_arguments keys must be in {SUPPORTED_GRADIENT_KWARGS}, got unexpected values: {set(self.gradient_keyword_arguments) - set(SUPPORTED_GRADIENT_KWARGS)}"
            )


DefaultExecutionConfig = ExecutionConfig()

_modules/pennylane/devices/execution_config

 

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