Source code for pennylane.transforms.convert_to_numpy_parameters

# 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


# 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.
This file contains preprocessings steps that may be called internally
during execution.
import pennylane as qml
from pennylane import math
from pennylane.tape import QuantumScript

# pylint: disable=no-member
def _convert_op_to_numpy_data(op: qml.operation.Operator) -> qml.operation.Operator:
    if math.get_interface(* == "numpy":
        return op
    # Use operator method to change parameters when it become available
    return qml.ops.functions.bind_new_parameters(op, math.unwrap(

# pylint: disable=no-member
def _convert_measurement_to_numpy_data(
    m: qml.measurements.MeasurementProcess,
) -> qml.measurements.MeasurementProcess:
    if m.obs is None:
        if m.eigvals() is None or math.get_interface(m.eigvals()) == "numpy":
            return m
        return type(m)(wires=m.wires, eigvals=math.unwrap(m.eigvals()))

    if math.get_interface(* == "numpy":
        return m
    new_obs = qml.ops.functions.bind_new_parameters(m.obs, math.unwrap(
    return type(m)(obs=new_obs)

# pylint: disable=protected-access
[docs]def convert_to_numpy_parameters(circuit: QuantumScript) -> QuantumScript: """Transforms a circuit to one with purely numpy parameters. Args: circuit (QuantumScript): a circuit with parameters of any interface Returns: QuantumScript: A circuit with purely numpy parameters .. seealso:: :class:`pennylane.tape.Unwrap` modifies a :class:`~.pennylane.tape.QuantumScript` in place instead of creating a new class. It will also set all parameters on the circuit, not just ones that need to be unwrapped. >>> ops = [qml.S(0), qml.RX(torch.tensor(0.1234), 0)] >>> measurements = [qml.state(), qml.expval(qml.Hermitian(torch.eye(2), 0))] >>> circuit = qml.tape.QuantumScript(ops, measurements ) >>> new_circuit = convert_to_numpy_parameters(circuit) >>> new_circuit.circuit [S(wires=[0]), RX(0.1234000027179718, wires=[0]), state(wires=[]), expval(Hermitian(array([[1., 0.], [0., 1.]], dtype=float32), wires=[0]))] If the component's data does not need to be transformed, it is left uncopied. >>> circuit[0] is new_circuit[0] True >>> circuit[1] is new_circuit[1] False >>> circuit[2] is new_circuit[2] True >>> circuit[3] is new_circuit[3] False """ new_ops = (_convert_op_to_numpy_data(op) for op in circuit.operations) new_measurements = (_convert_measurement_to_numpy_data(m) for m in circuit.measurements) new_circuit = circuit.__class__(new_ops, new_measurements, shots=circuit.shots) # must preserve trainable params as we lose information about the machine learning interface new_circuit.trainable_params = circuit.trainable_params return new_circuit