Source code for pennylane.templates.state_preparations.basis_qutrit

# Copyright 2018-2021 Xanadu Quantum Technologies Inc.

# Licensed under the Apache License, Version 2.0 (the "License");
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Contains the BasisStatePreparation template.

import pennylane as qml
from pennylane.operation import Operation, AnyWires

[docs]class QutritBasisStatePreparation(Operation): r""" Prepares a basis state on the given wires using a sequence of TShift gates. .. warning:: ``basis_state`` influences the circuit architecture and is therefore incompatible with gradient computations. Args: basis_state (array): Input array of shape ``(n,)``, where n is the number of wires the state preparation acts on. wires (Iterable): wires that the template acts on **Example** .. code-block:: python dev = qml.device("default.qutrit", wires=4) @qml.qnode(dev) def circuit(basis_state, obs): qml.QutritBasisStatePreparation(basis_state, wires=range(4)) return [qml.expval(qml.THermitian(obs, wires=i)) for i in range(4)] basis_state = [0, 1, 1, 0] obs = np.array([[1, 1, 0], [1, -1, 0], [0, 0, np.sqrt(2)]]) / np.sqrt(2) >>> print(circuit(basis_state, obs)) [array(0.70710678), array(-0.70710678), array(-0.70710678), array(0.70710678)] """ num_params = 1 num_wires = AnyWires grad_method = None def __init__(self, basis_state, wires, id=None): basis_state = qml.math.stack(basis_state) # check if the `basis_state` param is batched batched = len(qml.math.shape(basis_state)) > 1 state_batch = basis_state if batched else [basis_state] for i, state in enumerate(state_batch): shape = qml.math.shape(state) if len(shape) != 1: raise ValueError( f"Basis states must be one-dimensional; state {i} has shape {shape}." ) n_bits = shape[0] if n_bits != len(wires): raise ValueError( f"Basis states must be of length {len(wires)}; state {i} has length {n_bits}." ) if any(bit not in [0, 1, 2] for bit in state): raise ValueError( f"Basis states must only consist of 0s, 1s, and 2s; state {i} is {state}" ) # TODO: basis_state should be a hyperparameter, not a trainable parameter. # However, this breaks a test that ensures compatibility with batch_transform. # The transform should be rewritten to support hyperparameters as well. super().__init__(basis_state, wires=wires, id=id)
[docs] @staticmethod def compute_decomposition(basis_state, wires): # pylint: disable=arguments-differ r"""Representation of the operator as a product of other operators. .. math:: O = O_1 O_2 \dots O_n. .. seealso:: :meth:`~.BasisState.decomposition`. Args: basis_state (array): Input array of shape ``(len(wires),)`` wires (Any or Iterable[Any]): wires that the operator acts on Returns: list[.Operator]: decomposition of the operator **Example** >>> qml.QutritBasisStatePreparation.compute_decomposition(basis_state=[1, 2], wires=["a", "b"]) [Tshift(wires=['a']), Tshift(wires=['b']), TShift(wires=['b'])] """ op_list = [] for wire, state in zip(wires, basis_state): for _ in range(0, state): op_list.append(qml.TShift(wire)) return op_list