qml.templates.layers.RandomLayers

class RandomLayers(weights, wires, ratio_imprim=0.3, imprimitive=None, rotations=None, seed=42, do_queue=True, id=None)[source]

Bases: pennylane.operation.Operation

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 \(L\) 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 \(R_y, R_z\), controlled-Z gates as imprimitives, as well as ratio_imprim=0.3:

../../_images/layer_rnd.png
Parameters
  • 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 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

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.

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:

@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:

@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.

@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 shape() and used when creating randomly initialised weight tensors:

shape = RandomLayers.shape(n_layers=2, n_rotations=3)
weights = np.random.random(size=shape)

base_name

Get base name of the operator.

basis

The basis of an operation, or for controlled gates, of the target operation.

control_wires

For operations that are controlled, returns the set of control wires.

eigvals

Eigenvalues of an instantiated operator.

generator

Generator of the operation.

grad_method

grad_recipe

Gradient recipe for the parameter-shift method.

hash

returns an integer hash uniquely representing the operator

id

String for the ID of the operator.

inverse

Boolean determining if the inverse of the operation was requested.

is_composable_rotation

True if composing multiple copies of the operation results in an addition (or alternative accumulation) of parameters.

is_self_inverse

True if the operation is its own inverse.

is_symmetric_over_all_wires

True if the operation is the same if you exchange the order of wires.

is_symmetric_over_control_wires

True if the operation is the same if you exchange the order of all but the last wire.

matrix

Matrix representation of an instantiated operator in the computational basis.

name

Get and set the name of the operator.

num_params

num_wires

par_domain

parameters

Current parameter values.

single_qubit_rot_angles

The parameters required to implement a single-qubit gate as an equivalent Rot gate, up to a global phase.

string_for_inverse

wires

Wires of this operator.

base_name

Get base name of the operator.

basis = None

The basis of an operation, or for controlled gates, of the target operation. If not None, should take a value of "X", "Y", or "Z".

For example, X and CNOT have basis = "X", whereas ControlledPhaseShift and RZ have basis = "Z".

Type

str or None

control_wires

For operations that are controlled, returns the set of control wires.

Returns

The set of control wires of the operation.

Return type

Wires

eigvals
generator

Generator of the operation.

A length-2 list [generator, scaling_factor], where

  • generator is an existing PennyLane operation class or \(2\times 2\) Hermitian array that acts as the generator of the current operation

  • scaling_factor represents a scaling factor applied to the generator operation

For example, if \(U(\theta)=e^{i0.7\theta \sigma_x}\), then \(\sigma_x\), with scaling factor \(s\), is the generator of operator \(U(\theta)\):

generator = [PauliX, 0.7]

Default is [None, 1], indicating the operation has no generator.

grad_method = None
grad_recipe = None

Gradient recipe for the parameter-shift method.

This is a tuple with one nested list per operation parameter. For parameter \(\phi_k\), the nested list contains elements of the form \([c_i, a_i, s_i]\) where \(i\) is the index of the term, resulting in a gradient recipe of

\[\frac{\partial}{\partial\phi_k}f = \sum_{i} c_i f(a_i \phi_k + s_i).\]

If None, the default gradient recipe containing the two terms \([c_0, a_0, s_0]=[1/2, 1, \pi/2]\) and \([c_1, a_1, s_1]=[-1/2, 1, -\pi/2]\) is assumed for every parameter.

Type

tuple(Union(list[list[float]], None)) or None

hash

returns an integer hash uniquely representing the operator

Type

int

id

String for the ID of the operator.

inverse

Boolean determining if the inverse of the operation was requested.

is_composable_rotation = None

True if composing multiple copies of the operation results in an addition (or alternative accumulation) of parameters.

For example, qml.RZ is a composable rotation. Applying qml.RZ(0.1, wires=0) followed by qml.RZ(0.2, wires=0) is equivalent to performing a single rotation qml.RZ(0.3, wires=0).

If set to None, the operation will be ignored during compilation transforms that merge adjacent rotations.

Type

bool or None

is_self_inverse = None

True if the operation is its own inverse.

If None, all instances of the given operation will be ignored during compilation transforms involving inverse cancellation.

Type

bool or None

is_symmetric_over_all_wires = None

True if the operation is the same if you exchange the order of wires.

For example, qml.CZ(wires=[0, 1]) has the same effect as qml.CZ(wires=[1, 0]) due to symmetry of the operation.

If None, all instances of the operation will be ignored during compilation transforms that check for wire symmetry.

Type

bool or None

is_symmetric_over_control_wires = None

True if the operation is the same if you exchange the order of all but the last wire.

For example, qml.Toffoli(wires=[0, 1, 2]) has the same effect as qml.Toffoli(wires=[1, 0, 2]), but neither are the same as qml.Toffoli(wires=[0, 2, 1]).

If None, all instances of the operation will be ignored during compilation transforms that check for control-wire symmetry.

Type

bool or None

matrix
name

Get and set the name of the operator.

num_params = 1
num_wires = -1
par_domain = 'A'
parameters

Current parameter values.

single_qubit_rot_angles

The parameters required to implement a single-qubit gate as an equivalent Rot gate, up to a global phase.

Returns

A list of values \([\phi, \theta, \omega]\) such that \(RZ(\omega) RY(\theta) RZ(\phi)\) is equivalent to the original operation.

Return type

tuple[float, float, float]

string_for_inverse = '.inv'
wires

Wires of this operator.

Returns

wires

Return type

Wires

adjoint([do_queue])

Create an operation that is the adjoint of this one.

decomposition(*params, wires)

Returns a template decomposing the operation into other quantum operations.

expand()

Returns a tape containing the decomposed operations, rather than a list.

get_parameter_shift(idx[, shift])

Multiplier and shift for the given parameter, based on its gradient recipe.

inv()

Inverts the operation, such that the inverse will be used for the computations by the specific device.

queue([context])

Append the operator to the Operator queue.

shape(n_layers, n_rotations)

Returns the expected shape of the weights tensor.

adjoint(do_queue=False)

Create an operation that is the adjoint of this one.

Adjointed operations are the conjugated and transposed version of the original operation. Adjointed ops are equivalent to the inverted operation for unitary gates.

Parameters

do_queue – Whether to add the adjointed gate to the context queue.

Returns

The adjointed operation.

static decomposition(*params, wires)

Returns a template decomposing the operation into other quantum operations.

expand()[source]

Returns a tape containing the decomposed operations, rather than a list.

Returns

Returns a quantum tape that contains the operations decomposition, or if not implemented, simply the operation itself.

Return type

JacobianTape

get_parameter_shift(idx, shift=1.5707963267948966)

Multiplier and shift for the given parameter, based on its gradient recipe.

Parameters

idx (int) – parameter index

Returns

list of multiplier, coefficient, shift for each term in the gradient recipe

Return type

list[[float, float, float]]

inv()

Inverts the operation, such that the inverse will be used for the computations by the specific device.

This method concatenates a string to the name of the operation, to indicate that the inverse will be used for computations.

Any subsequent call of this method will toggle between the original operation and the inverse of the operation.

Returns

operation to be inverted

Return type

Operator

queue(context=<class 'pennylane.queuing.QueuingContext'>)

Append the operator to the Operator queue.

static shape(n_layers, n_rotations)[source]

Returns the expected shape of the weights tensor.

Parameters
  • n_layers (int) – number of layers

  • n_rotations (int) – number of rotations

Returns

shape

Return type

tuple[int]