Source code for pennylane._device

# 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 :class:`Device` abstract base class.
"""
# pylint: disable=too-many-format-args, use-maxsplit-arg, protected-access
import abc
import types
import warnings
from collections import OrderedDict, namedtuple
from collections.abc import Iterable, Sequence
from functools import lru_cache

import numpy as np

import pennylane as qml
from pennylane.measurements import Expectation, MidMeasure, Probability, Sample, State, Variance
from pennylane.operation import Observable, Operation, Tensor
from pennylane.wires import WireError, Wires

ShotTuple = namedtuple("ShotTuple", ["shots", "copies"])
"""tuple[int, int]: Represents copies of a shot number."""


def _process_shot_sequence(shot_list):
    """Process the shot sequence, to determine the total
    number of shots and the shot vector.

    Args:
        shot_list (Sequence[int, tuple[int]]): sequence of non-negative shot integers

    Returns:
        tuple[int, list[.ShotTuple[int]]]: A tuple containing the total number
        of shots, as well as a list of shot tuples.

    **Example**

    >>> shot_list = [3, 1, 2, 2, 2, 2, 6, 1, 1, 5, 12, 10, 10]
    >>> _process_shot_sequence(shot_list)
    (57,
     [ShotTuple(shots=3, copies=1),
      ShotTuple(shots=1, copies=1),
      ShotTuple(shots=2, copies=4),
      ShotTuple(shots=6, copies=1),
      ShotTuple(shots=1, copies=2),
      ShotTuple(shots=5, copies=1),
      ShotTuple(shots=12, copies=1),
      ShotTuple(shots=10, copies=2)])

    The total number of shots (57), and a sparse representation of the shot
    sequence is returned, where tuples indicate the number of times a shot
    integer is repeated.
    """
    if all(isinstance(s, int) for s in shot_list):

        if len(set(shot_list)) == 1:
            # All shots are identical, only require a single shot tuple
            shot_vector = [ShotTuple(shots=shot_list[0], copies=len(shot_list))]
        else:
            # Iterate through the shots, and group consecutive identical shots
            split_at_repeated = np.split(shot_list, np.diff(shot_list).nonzero()[0] + 1)
            shot_vector = [ShotTuple(shots=i[0], copies=len(i)) for i in split_at_repeated]

    elif all(isinstance(s, (int, tuple)) for s in shot_list):
        # shot list contains tuples; assume it is already in a sparse representation
        shot_vector = [
            ShotTuple(*i) if isinstance(i, tuple) else ShotTuple(i, 1) for i in shot_list
        ]

    else:
        raise ValueError(f"Unknown shot sequence format {shot_list}")

    total_shots = int(np.sum(np.prod(shot_vector, axis=1)))
    return total_shots, shot_vector


[docs]class DeviceError(Exception): """Exception raised by a :class:`~.pennylane._device.Device` when it encounters an illegal operation in the quantum circuit. """
[docs]class Device(abc.ABC): """Abstract base class for PennyLane devices. Args: wires (int or Iterable[Number, str]]): Number of subsystems represented by the device, or iterable that contains unique labels for the subsystems as numbers (i.e., ``[-1, 0, 2]``) or strings (``['ancilla', 'q1', 'q2']``). Default 1 if not specified. shots (int): Number of circuit evaluations/random samples used to estimate expectation values of observables. Defaults to 1000 if not specified. """ # pylint: disable=too-many-public-methods,too-many-instance-attributes _capabilities = {"model": None, "supports_broadcasting": False} """The capabilities dictionary stores the properties of a device. Devices can add their own custom properties and overwrite existing ones by overriding the ``capabilities()`` method.""" _circuits = {} #: dict[str->Circuit]: circuit templates associated with this API class _asarray = staticmethod(np.asarray) def __init__(self, wires=1, shots=1000, *, analytic=None): self.shots = shots if analytic is not None: msg = "The analytic argument has been replaced by shots=None. " msg += "Please use shots=None instead of analytic=True." raise DeviceError(msg) if not isinstance(wires, Iterable): # interpret wires as the number of consecutive wires wires = range(wires) self._wires = Wires(wires) self.num_wires = len(self._wires) self._wire_map = self.define_wire_map(self._wires) self._num_executions = 0 self._op_queue = None self._obs_queue = None self._parameters = None self.tracker = qml.Tracker() self.custom_expand_fn = None def __repr__(self): """String representation.""" return f"<{self.__class__.__name__} device (wires={self.num_wires}, shots={self.shots}) at {hex(id(self))}>" def __str__(self): """Verbose string representation.""" package = self.__module__.split(".")[0] return ( f"{self.name}\nShort name: {self.short_name}\n" f"Package: {package}\n" f"Plugin version: {self.version}\n" f"Author: {self.author}\n" f"Wires: {self.num_wires}\n" f"Shots: {self.shots}" ) @property @abc.abstractmethod def name(self): """The full name of the device.""" @property @abc.abstractmethod def short_name(self): """Returns the string used to load the device.""" @property @abc.abstractmethod def pennylane_requires(self): """The current API version that the device plugin was made for.""" @property @abc.abstractmethod def version(self): """The current version of the plugin.""" @property @abc.abstractmethod def author(self): """The author(s) of the plugin.""" @property @abc.abstractmethod def operations(self): """Get the supported set of operations. Returns: set[str]: the set of PennyLane operation names the device supports """ @property @abc.abstractmethod def observables(self): """Get the supported set of observables. Returns: set[str]: the set of PennyLane observable names the device supports """ @property def shots(self): """Number of circuit evaluations/random samples used to estimate expectation values of observables""" return self._shots @property def analytic(self): """Whether shots is None or not. Kept for backwards compatability.""" if self._shots is None: return True return False @property def wires(self): """All wires that can be addressed on this device""" return self._wires @property def wire_map(self): """Ordered dictionary that defines the map from user-provided wire labels to the wire labels used on this device""" return self._wire_map @property def num_executions(self): """Number of times this device is executed by the evaluation of QNodes running on this device Returns: int: number of executions """ return self._num_executions @shots.setter def shots(self, shots): """Changes the number of shots. Args: shots (int): number of circuit evaluations/random samples used to estimate expectation values of observables Raises: DeviceError: if number of shots is less than 1 """ if shots is None: # device is in analytic mode self._shots = shots self._shot_vector = None self._raw_shot_sequence = None elif isinstance(shots, int): # device is in sampling mode (unbatched) if shots < 1: raise DeviceError( f"The specified number of shots needs to be at least 1. Got {shots}." ) self._shots = shots self._shot_vector = None elif isinstance(shots, Sequence) and not isinstance(shots, str): # device is in batched sampling mode self._shots, self._shot_vector = _process_shot_sequence(shots) self._raw_shot_sequence = shots else: raise DeviceError( "Shots must be a single non-negative integer or a sequence of non-negative integers." ) @property def shot_vector(self): """list[.ShotTuple[int, int]]: Returns the shot vector, a sparse representation of the shot sequence used by the device when evaluating QNodes. **Example** >>> dev = qml.device("default.qubit", wires=2, shots=[3, 1, 2, 2, 2, 2, 6, 1, 1, 5, 12, 10, 10]) >>> dev.shots 57 >>> dev.shot_vector [ShotTuple(shots=3, copies=1), ShotTuple(shots=1, copies=1), ShotTuple(shots=2, copies=4), ShotTuple(shots=6, copies=1), ShotTuple(shots=1, copies=2), ShotTuple(shots=5, copies=1), ShotTuple(shots=12, copies=1), ShotTuple(shots=10, copies=2)] The sparse representation of the shot sequence is returned, where tuples indicate the number of times a shot integer is repeated. """ return self._shot_vector def _has_partitioned_shots(self): """Checks if the device was instructed to perform executions with partitioned shots. Returns: bool: whether or not shots are partitioned """ return self._shot_vector is not None and ( len(self._shot_vector) > 1 or self._shot_vector[0].copies > 1 )
[docs] def define_wire_map(self, wires): """Create the map from user-provided wire labels to the wire labels used by the device. The default wire map maps the user wire labels to wire labels that are consecutive integers. However, by overwriting this function, devices can specify their preferred, non-consecutive and/or non-integer wire labels. Args: wires (Wires): user-provided wires for this device Returns: OrderedDict: dictionary specifying the wire map **Example** >>> dev = device('my.device', wires=['b', 'a']) >>> dev.wire_map() OrderedDict( [(<Wires = ['a']>, <Wires = [0]>), (<Wires = ['b']>, <Wires = [1]>)]) """ consecutive_wires = Wires(range(self.num_wires)) wire_map = zip(wires, consecutive_wires) return OrderedDict(wire_map)
[docs] def order_wires(self, subset_wires): """Given some subset of device wires return a Wires object with the same wires; sorted according to the device wire map. Args: subset_wires (Wires): The subset of device wires (in any order). Raise: ValueError: Could not find some or all subset wires subset_wires in device wires device_wires. Return: ordered_wires (Wires): a new Wires object containing the re-ordered wires set """ subset_lst = subset_wires.tolist() try: ordered_subset_lst = sorted(subset_lst, key=lambda label: self.wire_map[label]) except KeyError as e: raise ValueError( f"Could not find some or all subset wires {subset_wires} in device wires {self.wires}" ) from e return Wires(ordered_subset_lst)
[docs] @lru_cache() def map_wires(self, wires): """Map the wire labels of wires using this device's wire map. Args: wires (Wires): wires whose labels we want to map to the device's internal labelling scheme Returns: Wires: wires with new labels """ try: mapped_wires = wires.map(self.wire_map) except WireError as e: raise WireError( f"Did not find some of the wires {wires} on device with wires {self.wires}." ) from e return mapped_wires
[docs] @classmethod def capabilities(cls): """Get the capabilities of this device class. Inheriting classes that change or add capabilities must override this method, for example via .. code-block:: python @classmethod def capabilities(cls): capabilities = super().capabilities().copy() capabilities.update( supports_inverse_operations=False, supports_a_new_capability=True, ) return capabilities Returns: dict[str->*]: results """ return cls._capabilities
# pylint: disable=too-many-branches,unused-argument
[docs] def execute(self, queue, observables, parameters=None, **kwargs): """Execute a queue of quantum operations on the device and then measure the given observables. For plugin developers: Instead of overwriting this, consider implementing a suitable subset of :meth:`pre_apply`, :meth:`apply`, :meth:`post_apply`, :meth:`pre_measure`, :meth:`expval`, :meth:`var`, :meth:`sample`, :meth:`post_measure`, and :meth:`execution_context`. Args: queue (Iterable[~.operation.Operation]): operations to execute on the device observables (Iterable[~.operation.Observable]): observables to measure and return parameters (dict[int, list[ParameterDependency]]): Mapping from free parameter index to the list of :class:`Operations <pennylane.operation.Operation>` (in the queue) that depend on it. Keyword Args: return_native_type (bool): If True, return the result in whatever type the device uses internally, otherwise convert it into array[float]. Default: False. Raises: QuantumFunctionError: if the value of :attr:`~.Observable.return_type` is not supported Returns: array[float]: measured value(s) """ self.check_validity(queue, observables) self._op_queue = queue self._obs_queue = observables self._parameters = {} if parameters is not None: self._parameters.update(parameters) results = [] if self._shot_vector is not None: # The following warning assumes that QubitDevice.execute is stand-alone warnings.warn( "Specifying a list of shots is only supported for " "QubitDevice based devices. Falling back to executions using all shots in the shot list." ) with self.execution_context(): self.pre_apply() for operation in queue: self.apply(operation.name, operation.wires, operation.parameters) self.post_apply() self.pre_measure() for obs in observables: if isinstance(obs, Tensor): wires = [ob.wires for ob in obs.obs] else: wires = obs.wires if obs.return_type is Expectation: results.append(self.expval(obs.name, wires, obs.parameters)) elif obs.return_type is Variance: results.append(self.var(obs.name, wires, obs.parameters)) elif obs.return_type is Sample: results.append(np.array(self.sample(obs.name, wires, obs.parameters))) elif obs.return_type is Probability: results.append(list(self.probability(wires=wires).values())) elif obs.return_type is State: raise qml.QuantumFunctionError("Returning the state is not supported") elif obs.return_type is not None: raise qml.QuantumFunctionError( f"Unsupported return type specified for observable {obs.name}" ) self.post_measure() self._op_queue = None self._obs_queue = None self._parameters = None # increment counter for number of executions of device self._num_executions += 1 if self.tracker.active: self.tracker.update(executions=1, shots=self._shots) self.tracker.record() # Ensures that a combination with sample does not put # expvals and vars in superfluous arrays if all(obs.return_type is Sample for obs in observables): return self._asarray(results) if any(obs.return_type is Sample for obs in observables): return self._asarray(results, dtype="object") return self._asarray(results)
[docs] def batch_execute(self, circuits): """Execute a batch of quantum circuits on the device. The circuits are represented by tapes, and they are executed one-by-one using the device's ``execute`` method. The results are collected in a list. For plugin developers: This function should be overwritten if the device can efficiently run multiple circuits on a backend, for example using parallel and/or asynchronous executions. Args: circuits (list[.tape.QuantumTape]): circuits to execute on the device Returns: list[array[float]]: list of measured value(s) """ results = [] for circuit in circuits: # we need to reset the device here, else it will # not start the next computation in the zero state self.reset() res = self.execute(circuit.operations, circuit.observables) results.append(res) if self.tracker.active: self.tracker.update(batches=1, batch_len=len(circuits)) self.tracker.record() return results
[docs] def execute_and_gradients(self, circuits, method="jacobian", **kwargs): """Execute a batch of quantum circuits on the device, and return both the results and the gradients. The circuits are represented by tapes, and they are executed one-by-one using the device's ``execute`` method. The results and the corresponding Jacobians are collected in a list. For plugin developers: This method should be overwritten if the device can efficiently run multiple circuits on a backend, for example using parallel and/or asynchronous executions, and return both the results and the Jacobians. Args: circuits (list[.tape.QuantumTape]): circuits to execute on the device method (str): the device method to call to compute the Jacobian of a single circuit **kwargs: keyword argument to pass when calling ``method`` Returns: tuple[list[array[float]], list[array[float]]]: Tuple containing list of measured value(s) and list of Jacobians. Returned Jacobians should be of shape ``(output_shape, num_params)``. """ gradient_method = getattr(self, method) res = [] jacs = [] for circuit in circuits: # Evaluations and gradients are paired, so that # devices can re-use the device state for the # gradient computation (if applicable). res.append(self.batch_execute([circuit])[0]) jacs.append(gradient_method(circuit, **kwargs)) return res, jacs
[docs] def gradients(self, circuits, method="jacobian", **kwargs): """Return the gradients of a batch of quantum circuits on the device. The gradient method ``method`` is called sequentially for each circuit, and the corresponding Jacobians are collected in a list. For plugin developers: This method should be overwritten if the device can efficiently compute the gradient of multiple circuits on a backend, for example using parallel and/or asynchronous executions. Args: circuits (list[.tape.QuantumTape]): circuits to execute on the device method (str): the device method to call to compute the Jacobian of a single circuit **kwargs: keyword argument to pass when calling ``method`` Returns: list[array[float]]: List of Jacobians. Returned Jacobians should be of shape ``(output_shape, num_params)``. """ gradient_method = getattr(self, method) return [gradient_method(circuit, **kwargs) for circuit in circuits]
@property def stopping_condition(self): """.BooleanFn: Returns the stopping condition for the device. The returned function accepts a queuable object (including a PennyLane operation and observable) and returns ``True`` if supported by the device.""" return qml.BooleanFn( lambda obj: not isinstance(obj, qml.tape.QuantumTape) and self.supports_operation(obj.name) )
[docs] def custom_expand(self, fn): """Register a custom expansion function for the device. **Example** .. code-block:: python dev = qml.device("default.qubit", wires=2) @dev.custom_expand def my_expansion_function(self, tape, max_expansion=10): ... # can optionally call the default device expansion tape = self.default_expand_fn(tape, max_expansion=max_expansion) return tape The custom device expansion function must have arguments ``self`` (the device object), ``tape`` (the input circuit to transform and execute), and ``max_expansion`` (the number of times the circuit should be expanded). The default :meth:`~.default_expand_fn` method of the original device may be called. It is highly recommended to call this before returning, to ensure that the expanded circuit is supported on the device. """ self.custom_expand_fn = types.MethodType(fn, self)
[docs] def default_expand_fn(self, circuit, max_expansion=10): """Method for expanding or decomposing an input circuit. This method should be overwritten if custom expansion logic is required. By default, this method expands the tape if: - nested tapes are present, - any operations are not supported on the device, or - multiple observables are measured on the same wire. Args: circuit (.QuantumTape): the circuit to expand. max_expansion (int): The number of times the circuit should be expanded. Expansion occurs when an operation or measurement is not supported, and results in a gate decomposition. If any operations in the decomposition remain unsupported by the device, another expansion occurs. Returns: .QuantumTape: The expanded/decomposed circuit, such that the device will natively support all operations. """ # pylint: disable=protected-access obs_on_same_wire = len(circuit._obs_sharing_wires) > 0 obs_on_same_wire &= not any( isinstance(o, qml.Hamiltonian) for o in circuit._obs_sharing_wires ) ops_not_supported = not all(self.stopping_condition(op) for op in circuit.operations) if ops_not_supported or obs_on_same_wire: circuit = circuit.expand(depth=max_expansion, stop_at=self.stopping_condition) return circuit
[docs] def expand_fn(self, circuit, max_expansion=10): """Method for expanding or decomposing an input circuit. Can be the default or a custom expansion method, see :meth:`.Device.default_expand_fn` and :meth:`.Device.custom_expand` for more details. Args: circuit (.QuantumTape): the circuit to expand. max_expansion (int): The number of times the circuit should be expanded. Expansion occurs when an operation or measurement is not supported, and results in a gate decomposition. If any operations in the decomposition remain unsupported by the device, another expansion occurs. Returns: .QuantumTape: The expanded/decomposed circuit, such that the device will natively support all operations. """ if self.custom_expand_fn is not None: return self.custom_expand_fn(circuit, max_expansion=max_expansion) return self.default_expand_fn(circuit, max_expansion=max_expansion)
[docs] def batch_transform(self, circuit): """Apply a differentiable batch transform for preprocessing a circuit prior to execution. This method is called directly by the QNode, and should be overwritten if the device requires a transform that generates multiple circuits prior to execution. By default, this method contains logic for generating multiple circuits, one per term, of a circuit that terminates in ``expval(H)``, if the underlying device does not support Hamiltonian expectation values, or if the device requires finite shots. .. warning:: This method will be tracked by autodifferentiation libraries, such as Autograd, JAX, TensorFlow, and Torch. Please make sure to use ``qml.math`` for autodiff-agnostic tensor processing if required. Args: circuit (.QuantumTape): the circuit to preprocess Returns: tuple[Sequence[.QuantumTape], callable]: Returns a tuple containing the sequence of circuits to be executed, and a post-processing function to be applied to the list of evaluated circuit results. """ supports_hamiltonian = self.supports_observable("Hamiltonian") finite_shots = self.shots is not None grouping_known = all( obs.grouping_indices is not None for obs in circuit.observables if obs.name == "Hamiltonian" ) hamiltonian_in_obs = "Hamiltonian" in [obs.name for obs in circuit.observables] return_types = [m.return_type for m in circuit.observables] if hamiltonian_in_obs and ((not supports_hamiltonian or finite_shots) or grouping_known): # If the observable contains a Hamiltonian and the device does not # support Hamiltonians, or if the simulation uses finite shots, or # if the Hamiltonian explicitly specifies an observable grouping, # split tape into multiple tapes of diagonalizable known observables. try: circuits, hamiltonian_fn = qml.transforms.hamiltonian_expand(circuit, group=False) except ValueError as e: raise ValueError( "Can only return the expectation of a single Hamiltonian observable" ) from e elif ( len(circuit._obs_sharing_wires) > 0 and not hamiltonian_in_obs and qml.measurements.Sample not in return_types and qml.measurements.Probability not in return_types and qml.measurements.Counts not in return_types ): # Check for case of non-commuting terms and that there are no Hamiltonians # TODO: allow for Hamiltonians in list of observables as well. circuits, hamiltonian_fn = qml.transforms.split_non_commuting(circuit) else: # otherwise, return the output of an identity transform circuits = [circuit] def hamiltonian_fn(res): return res[0] # Check whether the circuit was broadcasted (then the Hamiltonian-expanded # ones will be as well) and whether broadcasting is supported if circuit.batch_size is None or self.capabilities().get("supports_broadcasting"): # If the circuit wasn't broadcasted or broadcasting is supported, no action required return circuits, hamiltonian_fn # Expand each of the broadcasted Hamiltonian-expanded circuits expanded_tapes, expanded_fn = qml.transforms.map_batch_transform( qml.transforms.broadcast_expand, circuits ) # Chain the postprocessing functions of the broadcasted-tape expansions and the Hamiltonian # expansion. Note that the application order is reversed compared to the expansion order, # i.e. while we first applied `hamiltonian_expand` to the tape, we need to process the # results from the broadcast expansion first. def total_processing(results): return hamiltonian_fn(expanded_fn(results)) return expanded_tapes, total_processing
@property def op_queue(self): """The operation queue to be applied. Note that this property can only be accessed within the execution context of :meth:`~.execute`. Raises: ValueError: if outside of the execution context Returns: list[~.operation.Operation] """ if self._op_queue is None: raise ValueError("Cannot access the operation queue outside of the execution context!") return self._op_queue @property def obs_queue(self): """The observables to be measured and returned. Note that this property can only be accessed within the execution context of :meth:`~.execute`. Raises: ValueError: if outside of the execution context Returns: list[~.operation.Observable] """ if self._obs_queue is None: raise ValueError( "Cannot access the observable value queue outside of the execution context!" ) return self._obs_queue @property def parameters(self): """Mapping from free parameter index to the list of :class:`Operations <~.Operation>` in the device queue that depend on it. Note that this property can only be accessed within the execution context of :meth:`~.execute`. Raises: ValueError: if outside of the execution context Returns: dict[int->list[ParameterDependency]]: the mapping """ if self._parameters is None: raise ValueError( "Cannot access the free parameter mapping outside of the execution context!" ) return self._parameters
[docs] def pre_apply(self): """Called during :meth:`execute` before the individual operations are executed."""
[docs] def post_apply(self): """Called during :meth:`execute` after the individual operations have been executed."""
[docs] def pre_measure(self): """Called during :meth:`execute` before the individual observables are measured."""
[docs] def post_measure(self): """Called during :meth:`execute` after the individual observables have been measured."""
[docs] def execution_context(self): """The device execution context used during calls to :meth:`execute`. You can overwrite this function to return a context manager in case your quantum library requires that; all operations and method calls (including :meth:`apply` and :meth:`expval`) are then evaluated within the context of this context manager (see the source of :meth:`.Device.execute` for more details). """ # pylint: disable=no-self-use class MockContext: # pylint: disable=too-few-public-methods """Mock class as a default for the with statement in execute().""" def __enter__(self): pass def __exit__(self, type, value, traceback): pass return MockContext()
[docs] def supports_operation(self, operation): """Checks if an operation is supported by this device. Args: operation (type or str): operation to be checked Raises: ValueError: if `operation` is not a :class:`~.Operation` class or string Returns: bool: ``True`` iff supplied operation is supported """ if isinstance(operation, type) and issubclass(operation, Operation): return operation.__name__ in self.operations if isinstance(operation, str): if operation.endswith(".inv"): in_ops = operation[:-4] in self.operations # TODO: update when all capabilities keys changed to "supports_inverse_operations" supports_inv = self.capabilities().get( "supports_inverse_operations", False ) or self.capabilities().get("inverse_operations", False) return in_ops and supports_inv return operation in self.operations raise ValueError( "The given operation must either be a pennylane.Operation class or a string." )
[docs] def supports_observable(self, observable): """Checks if an observable is supported by this device. Raises a ValueError, if not a subclass or string of an Observable was passed. Args: observable (type or str): observable to be checked Raises: ValueError: if `observable` is not a :class:`~.Observable` class or string Returns: bool: ``True`` iff supplied observable is supported """ if isinstance(observable, type) and issubclass(observable, Observable): return observable.__name__ in self.observables if isinstance(observable, str): # This check regards observables that are also operations if observable.endswith(".inv"): return self.supports_operation(observable[:-4]) return observable in self.observables raise ValueError( "The given observable must either be a pennylane.Observable class or a string." )
[docs] def check_validity(self, queue, observables): """Checks whether the operations and observables in queue are all supported by the device. Includes checks for inverse operations. Args: queue (Iterable[~.operation.Operation]): quantum operation objects which are intended to be applied on the device observables (Iterable[~.operation.Observable]): observables which are intended to be evaluated on the device Raises: DeviceError: if there are operations in the queue or observables that the device does not support """ for o in queue: operation_name = o.name if getattr(o, "return_type", None) == MidMeasure and not self.capabilities().get( "supports_mid_measure", False ): raise DeviceError( f"Mid-circuit measurements are not natively supported on device {self.short_name}. " "Apply the @qml.defer_measurements decorator to your quantum function to " "simulate the application of mid-circuit measurements on this device." ) if getattr(o, "inverse", False): # TODO: update when all capabilities keys changed to "supports_inverse_operations" supports_inv = self.capabilities().get( "supports_inverse_operations", False ) or self.capabilities().get("inverse_operations", False) if not supports_inv: raise DeviceError( f"The inverse of gates are not supported on device {self.short_name}" ) operation_name = o.base_name if not self.stopping_condition(o): raise DeviceError( f"Gate {operation_name} not supported on device {self.short_name}" ) for o in observables: if isinstance(o, qml.measurements.MeasurementProcess) and o.obs is not None: o = o.obs if isinstance(o, Tensor): # TODO: update when all capabilities keys changed to "supports_tensor_observables" supports_tensor = self.capabilities().get( "supports_tensor_observables", False ) or self.capabilities().get("tensor_observables", False) if not supports_tensor: raise DeviceError( f"Tensor observables not supported on device {self.short_name}" ) for i in o.obs: if not self.supports_observable(i.name): raise DeviceError( f"Observable {i.name} not supported on device {self.short_name}" ) else: observable_name = o.name if issubclass(o.__class__, Operation) and o.inverse: # TODO: update when all capabilities keys changed to "supports_inverse_operations" supports_inv = self.capabilities().get( "supports_inverse_operations", False ) or self.capabilities().get("inverse_operations", False) if not supports_inv: raise DeviceError( f"The inverse of gates are not supported on device {self.short_name}" ) observable_name = o.base_name if not self.supports_observable(observable_name): raise DeviceError( f"Observable {observable_name} not supported on device {self.short_name}" )
[docs] @abc.abstractmethod def apply(self, operation, wires, par): """Apply a quantum operation. For plugin developers: this function should apply the operation on the device. Args: operation (str): name of the operation wires (Wires): wires that the operation is applied to par (tuple): parameters for the operation """
[docs] @abc.abstractmethod def expval(self, observable, wires, par): r"""Returns the expectation value of observable on specified wires. Note: all arguments accept _lists_, which indicate a tensor product of observables. Args: observable (str or list[str]): name of the observable(s) wires (Wires): wires the observable(s) are to be measured on par (tuple or list[tuple]]): parameters for the observable(s) Returns: float: expectation value :math:`\expect{A} = \bra{\psi}A\ket{\psi}` """
[docs] def var(self, observable, wires, par): r"""Returns the variance of observable on specified wires. Note: all arguments support _lists_, which indicate a tensor product of observables. Args: observable (str or list[str]): name of the observable(s) wires (Wires): wires the observable(s) is to be measured on par (tuple or list[tuple]]): parameters for the observable(s) Raises: NotImplementedError: if the device does not support variance computation Returns: float: variance :math:`\mathrm{var}(A) = \bra{\psi}A^2\ket{\psi} - \bra{\psi}A\ket{\psi}^2` """ raise NotImplementedError( f"Returning variances from QNodes not currently supported by {self.short_name}" )
[docs] def sample(self, observable, wires, par): """Return a sample of an observable. The number of samples is determined by the value of ``Device.shots``, which can be directly modified. Note: all arguments support _lists_, which indicate a tensor product of observables. Args: observable (str or list[str]): name of the observable(s) wires (Wires): wires the observable(s) is to be measured on par (tuple or list[tuple]]): parameters for the observable(s) Raises: NotImplementedError: if the device does not support sampling Returns: array[float]: samples in an array of dimension ``(shots,)`` """ raise NotImplementedError( f"Returning samples from QNodes not currently supported by {self.short_name}" )
[docs] def probability(self, wires=None): """Return the (marginal) probability of each computational basis state from the last run of the device. Args: wires (Sequence[int]): Sequence of wires to return marginal probabilities for. Wires not provided are traced out of the system. Returns: OrderedDict[tuple, float]: Dictionary mapping a tuple representing the state to the resulting probability. The dictionary should be sorted such that the state tuples are in lexicographical order. """ raise NotImplementedError( f"Returning probability not currently supported by {self.short_name}" )
[docs] @abc.abstractmethod def reset(self): """Reset the backend state. After the reset, the backend should be as if it was just constructed. Most importantly the quantum state is reset to its initial value. """