Source code for pennylane.ops.functions.simplify

# 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.
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This module contains the qml.simplify function.
from copy import copy
from functools import wraps
from typing import Callable, Union

import pennylane as qml
from pennylane.measurements import MeasurementProcess
from pennylane.operation import Operator
from pennylane.qnode import QNode
from pennylane.queuing import QueuingManager
from pennylane.tape import QuantumScript, make_qscript, QuantumTape

[docs]def simplify(input: Union[Operator, MeasurementProcess, QuantumTape, QNode, Callable]): """Simplifies an operator, tape, qnode or quantum function by reducing its arithmetic depth or number of rotation parameters. Args: input (.Operator, pennylane.QNode, .QuantumTape, or Callable): an operator, quantum node, tape or function that applies quantum operations Returns: (.Operator, pennylane.QNode, .QuantumTape, or Callable): Simplified input. **Example** Given an instantiated operator, ``qml.simplify`` reduces the operator's arithmetic depth: >>> op = qml.adjoint(qml.RX(0.54, wires=0) + qml.PauliX(0) + qml.PauliZ(1)) >>> op.arithmetic_depth 3 >>> sim_op = qml.simplify(op) >>> sim_op.arithmetic_depth 2 >>> type(sim_op) pennylane.ops.op_math.sum.Sum >>> sim_op.operands (Adjoint(RX)(0.54, wires=[0]), Adjoint(PauliX)(wires=[0]), Adjoint(PauliZ)(wires=[1])) This function can also simplify the number of rotation gate parameters: >>> qml.simplify(qml.Rot(np.pi / 2, 0.1, -np.pi / 2, wires=0)) RX(0.1, wires=[0]) Both types of simplification occur together: >>> op = qml.adjoint(qml.U2(-np.pi/2, np.pi/2, wires=0) + qml.PauliX(0)) >>> op Adjoint(Sum)([-1.5707963267948966, 1.5707963267948966], [], wires=[0]) >>> qml.simplify(op) Adjoint(RX)(1.5707963267948966, wires=[0]) + Adjoint(PauliX)(wires=[0]) Moreover, ``qml.simplify`` can be used to simplify QNodes or quantum functions: >>> dev = qml.device("default.qubit", wires=2) >>> @qml.simplify @qml.qnode(dev) def circuit(): qml.adjoint(, 0) ** 1, qml.RY(1, 0), qml.RZ(1, 0))) return qml.probs(wires=0) >>> circuit() tensor([0.64596329, 0.35403671], requires_grad=True) >>> list(circuit.tape) [RZ(11.566370614359172, wires=[0]) @ RY(11.566370614359172, wires=[0]) @ RX(11.566370614359172, wires=[0]), probs(wires=[0])] """ if isinstance(input, (Operator, MeasurementProcess)): if QueuingManager.recording(): with QueuingManager.stop_recording(): new_op = copy(input.simplify()) QueuingManager.remove(input) return qml.apply(new_op) return input.simplify() if isinstance(input, QuantumScript): return input.__class__( [op.simplify() for op in input.operations], [m.simplify() for m in input.measurements], shots=input.shots, ) if callable(input): old_qfunc = input.func if isinstance(input, QNode) else input @wraps(old_qfunc) def qfunc(*args, **kwargs): qs = make_qscript(old_qfunc)(*args, **kwargs) _ = [qml.simplify(op) for op in qs.operations] m = tuple(qml.simplify(m) for m in qs.measurements) return m[0] if len(m) == 1 else m if isinstance(input, QNode): return QNode( func=qfunc, device=input.device, interface=input.interface, diff_method=input.diff_method, expansion_strategy=input.expansion_strategy, **input.execute_kwargs, **input.gradient_kwargs, ) return qfunc raise ValueError(f"Cannot simplify the object {input} of type {type(input)}.")