Source code for pennylane.templates.layers.random

# Copyright 2018-2021 Xanadu Quantum Technologies Inc.

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

[docs]class RandomLayers(Operation): r"""Layers of randomly chosen single qubit rotations and 2-qubit entangling gates, acting on randomly chosen qubits. .. warning:: This template uses random number generation inside qnodes. Find more details about how to invoke the desired random behaviour in the "Usage Details" section below. The argument ``weights`` contains the weights for each layer. The number of layers :math:`L` is therefore derived from the first dimension of ``weights``. The two-qubit gates of type ``imprimitive`` and the rotations are distributed randomly in the circuit. The number of random rotations is derived from the second dimension of ``weights``. The number of two-qubit gates is determined by ``ratio_imprim``. For example, a ratio of ``0.3`` with ``30`` rotations will lead to the use of ``10`` two-qubit gates. .. note:: If applied to one qubit only, this template will use no imprimitive gates. This is an example of two 4-qubit random layers with four Pauli-Y/Pauli-Z rotations :math:`R_y, R_z`, controlled-Z gates as imprimitives, as well as ``ratio_imprim=0.3``: .. figure:: ../../_static/layer_rnd.png :align: center :width: 60% :target: javascript:void(0); Args: weights (tensor_like): weight tensor of shape ``(L, k)``, wires (Iterable): wires that the template acts on ratio_imprim (float): value between 0 and 1 that determines the ratio of imprimitive to rotation gates imprimitive (pennylane.ops.Operation): two-qubit gate to use, defaults to :class:`~pennylane.ops.CNOT` rotations (list[pennylane.ops.Operation]): List of Pauli-X, Pauli-Y and/or Pauli-Z gates. The frequency determines how often a particular rotation type is used. Defaults to the use of all three rotations with equal frequency. seed (int): seed to generate random architecture, defaults to 42 .. UsageDetails:: **Default seed** ``RandomLayers`` always uses a seed to initialize the construction of a random circuit. This means that the template creates the same circuit every time it is called. If no seed is provided, the default seed of ``42`` is used. .. code-block:: python import pennylane as qml import numpy as np from pennylane.templates.layers import RandomLayers dev = qml.device("default.qubit", wires=2) weights = [[0.1, -2.1, 1.4]] @qml.qnode(dev) def circuit1(weights): RandomLayers(weights=weights, wires=range(2)) return qml.expval(qml.PauliZ(0)) @qml.qnode(dev) def circuit2(weights): RandomLayers(weights=weights, wires=range(2)) return qml.expval(qml.PauliZ(0)) >>> np.allclose(circuit1(weights), circuit2(weights)) True You can verify this by drawing the circuits. >>> print(circuit1.draw()) 0: ─────────────────────╭X──╭X──RZ(1.4)──┤ ⟨Z⟩ 1: ──RX(0.1)──RX(-2.1)──╰C──╰C───────────┤ >>> print(circuit2.draw()) 0: ─────────────────────╭X──╭X──RZ(1.4)──┤ ⟨Z⟩ 1: ──RX(0.1)──RX(-2.1)──╰C──╰C───────────┤ **Changing the seed** To change the randomly generated circuit architecture, you have to change the seed passed to the template. For example, these two calls of ``RandomLayers`` *do not* create the same circuit: .. code-block:: python @qml.qnode(dev) def circuit_9(weights): RandomLayers(weights=weights, wires=range(2), seed=9) return qml.expval(qml.PauliZ(0)) @qml.qnode(dev) def circuit_12(weights): RandomLayers(weights=weights, wires=range(2), seed=12) return qml.expval(qml.PauliZ(0)) >>> np.allclose(circuit_9(weights), circuit_12(weights)) >>> False >>> print(circuit_9.draw()) 0: ──╭X──RX(0.1)────────────┤ ⟨Z⟩ 1: ──╰C──RY(-2.1)──RX(1.4)──┤ >>> print(circuit_12.draw()) 0: ──╭X──RZ(0.1)───╭C──╭X───────────┤ ⟨Z⟩ 1: ──╰C──RX(-2.1)──╰X──╰C──RZ(1.4)──┤ **Automatic creation of random circuits** To automate the process of creating different circuits with ``RandomLayers``, you can set ``seed=None`` to avoid specifying a seed. However, in this case care needs to be taken. In the default setting, a quantum node is **mutable**, which means that the quantum function is re-evaluated every time it is called. This means that the circuit is re-constructed from scratch each time you call the qnode: .. code-block:: python @qml.qnode(dev) def circuit_rnd(weights): RandomLayers(weights=weights, wires=range(2), seed=None) return qml.expval(qml.PauliZ(0)) first_call = circuit_rnd(weights) second_call = circuit_rnd(weights) >>> np.allclose(first_call, second_call) False This can be rectified by making the quantum node **immutable**. .. code-block:: python @qml.qnode(dev, mutable=False) def circuit_rnd(weights): RandomLayers(weights=weights, wires=range(2), seed=None) return qml.expval(qml.PauliZ(0)) first_call = circuit_rnd(weights) second_call = circuit_rnd(weights) >>> np.allclose(first_call, second_call) True **Parameter shape** The expected shape for the weight tensor can be computed with the static method :meth:`~.RandomLayers.shape` and used when creating randomly initialised weight tensors: .. code-block:: python shape = RandomLayers.shape(n_layers=2, n_rotations=3) weights = np.random.random(size=shape) """ num_params = 1 num_wires = AnyWires par_domain = "A" def __init__( self, weights, wires, ratio_imprim=0.3, imprimitive=None, rotations=None, seed=42, do_queue=True, id=None, ): self.seed = seed self.rotations = rotations or [qml.RX, qml.RY, qml.RZ] shape = qml.math.shape(weights) if len(shape) != 2: raise ValueError(f"Weights tensor must be 2-dimensional; got shape {shape}") self.n_layers = shape[0] self.imprimitive = imprimitive or qml.CNOT self.ratio_imprimitive = ratio_imprim super().__init__(weights, wires=wires, do_queue=do_queue, id=id)
[docs] def expand(self): if self.seed is not None: np.random.seed(self.seed) shape = qml.math.shape(self.parameters[0]) with qml.tape.QuantumTape() as tape: for l in range(self.n_layers): i = 0 while i < shape[1]: if np.random.random() > self.ratio_imprimitive: # apply a random rotation gate to a random wire gate = np.random.choice(self.rotations) rnd_wire = self.wires.select_random(1) gate(self.parameters[0][l, i], wires=rnd_wire) i += 1 else: # apply the entangler to two random wires if len(self.wires) > 1: rnd_wires = self.wires.select_random(2) self.imprimitive(wires=rnd_wires) return tape
[docs] @staticmethod def shape(n_layers, n_rotations): r"""Returns the expected shape of the weights tensor. Args: n_layers (int): number of layers n_rotations (int): number of rotations Returns: tuple[int]: shape """ return n_layers, n_rotations