Source code for pennylane.templates.layers.simplified_two_design

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Contains the SimplifiedTwoDesign template.
# pylint: disable-msg=too-many-branches,too-many-arguments,protected-access
import pennylane as qml
from pennylane.operation import Operation, AnyWires

[docs]class SimplifiedTwoDesign(Operation): r""" Layers consisting of a simplified 2-design architecture of Pauli-Y rotations and controlled-Z entanglers proposed in `Cerezo et al. (2021) <>`_. A 2-design is an ensemble of unitaries whose statistical properties are the same as sampling random unitaries with respect to the Haar measure up to the first 2 moments. The template is not a strict 2-design, since it does not consist of universal 2-qubit gates as building blocks, but has been shown in `Cerezo et al. (2021) <>`_ to exhibit important properties to study "barren plateaus" in quantum optimization landscapes. The template starts with an initial layer of single qubit Pauli-Y rotations, before the main :math:`L` layers are applied. The basic building block of the main layers are controlled-Z entanglers followed by a pair of Pauli-Y rotation gates (one for each wire). Each layer consists of an "even" part whose entanglers start with the first qubit, and an "odd" part that starts with the second qubit. This is an example of two layers, including the initial layer: .. figure:: ../../_static/templates/layers/simplified_two_design.png :align: center :width: 40% :target: javascript:void(0); | The argument ``initial_layer_weights`` contains the rotation angles of the initial layer of Pauli-Y rotations, while ``weights`` contains the pairs of Pauli-Y rotation angles of the respective layers. Each layer takes :math:`\lfloor M/2 \rfloor + \lfloor (M-1)/2 \rfloor = M-1` pairs of angles, where :math:`M` is the number of wires. The number of layers :math:`L` is derived from the first dimension of ``weights``. Args: initial_layer_weights (tensor_like): weight tensor for the initial rotation block, shape ``(M,)`` weights (tensor_like): tensor of rotation angles for the layers, shape ``(L, M-1, 2)`` wires (Iterable): wires that the template acts on .. details:: :title: Usage Details template - here shown for two layers - is used inside a :class:`QNode <pennylane.QNode>`: .. code-block:: python import pennylane as qml from math import pi n_wires = 3 dev = qml.device('default.qubit', wires=n_wires) @qml.qnode(dev) def circuit(init_weights, weights): qml.SimplifiedTwoDesign(initial_layer_weights=init_weights, weights=weights, wires=range(n_wires)) return [qml.expval(qml.Z(i)) for i in range(n_wires)] init_weights = [pi, pi, pi] weights_layer1 = [[0., pi], [0., pi]] weights_layer2 = [[pi, 0.], [pi, 0.]] weights = [weights_layer1, weights_layer2] >>> circuit(init_weights, weights) [1., -1., 1.] **Parameter shapes** A list of shapes for the two weights arguments can be computed with the static method :meth:`~.qml.SimplifiedTwoDesign.shape` and used when creating randomly initialised weight tensors: .. code-block:: python shapes = qml.SimplifiedTwoDesign.shape(n_layers=2, n_wires=2) weights = [np.random.random(size=shape) for shape in shapes] """ num_wires = AnyWires grad_method = None def __init__(self, initial_layer_weights, weights, wires, id=None): shape = qml.math.shape(weights) if len(shape) > 1: if shape[1] != len(wires) - 1: raise ValueError( f"Weights tensor must have second dimension of length {len(wires) - 1}; got {shape[1]}" ) if shape[2] != 2: raise ValueError( f"Weights tensor must have third dimension of length 2; got {shape[2]}" ) shape2 = qml.math.shape(initial_layer_weights) if shape2 != (len(wires),): raise ValueError( f"Initial layer weights must be of shape {(len(wires),)}; got {shape2}" ) self.n_layers = shape[0] super().__init__(initial_layer_weights, weights, wires=wires, id=id) @property def num_params(self): return 2
[docs] @staticmethod def compute_decomposition( initial_layer_weights, weights, 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:`~.SimplifiedTwoDesign.decomposition`. Args: initial_layer_weights (tensor_like): weight tensor for the initial rotation block weights (tensor_like): tensor of rotation angles for the layers wires (Any or Iterable[Any]): wires that the operator acts on Returns: list[.Operator]: decomposition of the operator **Example** >>> qml.SimplifiedTwoDesign.compute_decomposition(initial_layer_weights, weights, wires=["a", "b", "c"]) [RY(tensor(3.1416), wires=['a']), RY(tensor(3.1416), wires=['b']), RY(tensor(3.1416), wires=['c']), CZ(wires=['a', 'b']), RY(tensor(0.), wires=['a']), RY(tensor(3.1416), wires=['b']), CZ(wires=['b', 'c']), RY(tensor(0.), wires=['b']), RY(tensor(3.1416), wires=['c']), CZ(wires=['a', 'b']), RY(tensor(3.1416), wires=['a']), RY(tensor(0.), wires=['b']), CZ(wires=['b', 'c']), RY(tensor(3.1416), wires=['b']), RY(tensor(0.), wires=['c'])] """ n_layers = qml.math.shape(weights)[0] op_list = [] # initial rotations for i in range(len(wires)): # pylint: disable=consider-using-enumerate op_list.append(qml.RY(initial_layer_weights[i], wires=wires[i])) for layer in range(n_layers): # even layer of entanglers even_wires = [wires[i : i + 2] for i in range(0, len(wires) - 1, 2)] for i, wire_pair in enumerate(even_wires): op_list.append(qml.CZ(wires=wire_pair)) op_list.append(qml.RY(weights[layer, i, 0], wires=wire_pair[0])) op_list.append(qml.RY(weights[layer, i, 1], wires=wire_pair[1])) # odd layer of entanglers odd_wires = [wires[i : i + 2] for i in range(1, len(wires) - 1, 2)] for i, wire_pair in enumerate(odd_wires): op_list.append(qml.CZ(wires=wire_pair)) op_list.append(qml.RY(weights[layer, len(wires) // 2 + i, 0], wires=wire_pair[0])) op_list.append(qml.RY(weights[layer, len(wires) // 2 + i, 1], wires=wire_pair[1])) return op_list
[docs] @staticmethod def shape(n_layers, n_wires): r"""Returns a list of shapes for the 2 parameter tensors. Args: n_layers (int): number of layers n_wires (int): number of wires Returns: list[tuple[int]]: list of shapes """ if n_wires == 1: return [(n_wires,), (n_layers,)] return [(n_wires,), (n_layers, n_wires - 1, 2)]