Source code for pennylane.transforms.specs

# 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


# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
"""Code for resource estimation"""
import inspect

import pennylane as qml

def _get_absolute_import_path(fn):
    return f"{inspect.getmodule(fn).__name__}.{fn.__name__}"

[docs]def specs(qnode, max_expansion=None, expansion_strategy=None): """Resource information about a quantum circuit. This transform converts a QNode into a callable that provides resource information about the circuit. Args: qnode (.QNode): the QNode to calculate the specifications for Keyword Args: max_expansion (int): The number of times the internal circuit should be expanded when calculating the specification. Defaults to ``qnode.max_expansion``. expansion_strategy (str): The strategy to use when circuit expansions or decompositions are required. - ``gradient``: The QNode will attempt to decompose the internal circuit such that all circuit operations are supported by the gradient method. - ``device``: The QNode will attempt to decompose the internal circuit such that all circuit operations are natively supported by the device. Returns: A function that has the same argument signature as ``qnode``. This function returns a dictionary of information about qnode structure. **Example** .. code-block:: python3 x = np.array([0.1, 0.2]) dev = qml.device('default.qubit', wires=2) @qml.qnode(dev, diff_method="parameter-shift", shifts=np.pi / 4) def circuit(x, add_ry=True): qml.RX(x[0], wires=0) qml.CNOT(wires=(0,1)) if add_ry: qml.RY(x[1], wires=1) return qml.probs(wires=(0,1)) >>> qml.specs(circuit)(x, add_ry=False) {'resources': Resources(num_wires=2, num_gates=2, gate_types=defaultdict(<class 'int'>, {'RX': 1, 'CNOT': 1}), gate_sizes=defaultdict(<class 'int'>, {1: 1, 2: 1}), depth=2, shots=Shots(total_shots=None, shot_vector=())), 'num_observables': 1, 'num_diagonalizing_gates': 0, 'num_trainable_params': 1, 'num_device_wires': 2, 'device_name': 'default.qubit', 'expansion_strategy': 'gradient', 'gradient_options': {'shifts': 0.7853981633974483}, 'interface': 'auto', 'diff_method': 'parameter-shift', 'gradient_fn': 'pennylane.transforms.core.transform_dispatcher.param_shift', 'num_gradient_executions': 2} """ def specs_qnode(*args, **kwargs): """Returns information on the structure and makeup of provided QNode. Dictionary keys: * ``"num_operations"`` number of operations in the qnode * ``"num_observables"`` number of observables in the qnode * ``"num_diagonalizing_gates"`` number of diagonalizing gates required for execution of the qnode * ``"resources"``: a :class:`~.resource.Resources` object containing resource quantities used by the qnode * ``"num_used_wires"``: number of wires used by the circuit * ``"num_device_wires"``: number of wires in device * ``"depth"``: longest path in directed acyclic graph representation * ``"device_name"``: name of QNode device * ``"expansion_strategy"``: string specifying method for decomposing operations in the circuit * ``"gradient_options"``: additional configurations for gradient computations * ``"interface"``: autodiff framework to dispatch to for the qnode execution * ``"diff_method"``: a string specifying the differntiation method * ``"gradient_fn"``: executable to compute the gradient of the qnode Potential Additional Information: * ``"num_trainable_params"``: number of individual scalars that are trainable * ``"num_gradient_executions"``: number of times circuit will execute when calculating the derivative Returns: dict[str, Union[defaultdict,int]]: dictionaries that contain QNode specifications """ initial_max_expansion = qnode.max_expansion initial_expansion_strategy = getattr(qnode, "expansion_strategy", None) try: qnode.max_expansion = initial_max_expansion if max_expansion is None else max_expansion qnode.expansion_strategy = expansion_strategy or initial_expansion_strategy qnode.construct(args, kwargs) finally: qnode.max_expansion = initial_max_expansion qnode.expansion_strategy = initial_expansion_strategy info = qnode.qtape.specs.copy() info["num_device_wires"] = ( len(qnode.tape.wires) if isinstance(qnode.device, qml.devices.Device) else len(qnode.device.wires) ) info["device_name"] = getattr(qnode.device, "short_name", info["expansion_strategy"] = qnode.expansion_strategy info["gradient_options"] = qnode.gradient_kwargs info["interface"] = qnode.interface info["diff_method"] = ( _get_absolute_import_path(qnode.diff_method) if callable(qnode.diff_method) else qnode.diff_method ) if isinstance(qnode.gradient_fn, qml.transforms.core.TransformDispatcher): info["gradient_fn"] = _get_absolute_import_path(qnode.gradient_fn) try: info["num_gradient_executions"] = len(qnode.gradient_fn(qnode.qtape)[0]) except Exception as e: # pylint: disable=broad-except # In the case of a broad exception, we don't want the `qml.specs` transform # to fail. Instead, we simply indicate that the number of gradient executions # is not supported for the reason specified. info["num_gradient_executions"] = f"NotSupported: {str(e)}" else: info["gradient_fn"] = qnode.gradient_fn return info return specs_qnode