qml.templates.layers.RandomLayers¶

class
RandomLayers
(weights, wires, ratio_imprim=0.3, imprimitive=None, rotations=None, seed=42, do_queue=True)[source]¶ Bases:
pennylane.operation.Operation
Layers of randomly chosen single qubit rotations and 2qubit 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 ofweights
.The twoqubit gates of type
imprimitive
and the rotations are distributed randomly in the circuit. The number of random rotations is derived from the second dimension ofweights
. The number of twoqubit gates is determined byratio_imprim
. For example, a ratio of0.3
with30
rotations will lead to the use of10
twoqubit gates.Note
If applied to one qubit only, this template will use no imprimitive gates.
This is an example of two 4qubit random layers with four PauliY/PauliZ rotations \(R_y, R_z\), controlledZ gates as imprimitives, as well as
ratio_imprim=0.3
: 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) – twoqubit gate to use, defaults to
CNOT
rotations (list[pennylane.ops.Operation]) – List of PauliX, PauliY and/or PauliZ 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
Usage Details
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 of42
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 setseed=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 reevaluated every time it is called. This means that the circuit is reconstructed 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)
Attributes
Get base name of the operator.
Eigenvalues of an instantiated operator.
Generator of the operation.
Gradient computation method.
Gradient recipe for the parametershift method.
Boolean determining if the inverse of the operation was requested.
Matrix representation of an instantiated operator in the computational basis.
Get and set the name of the operator.
Current parameter values.
Wires of this operator.

base_name
¶ Get base name of the operator.

eigvals
¶

generator
¶ Generator of the operation.
A length2 list
[generator, scaling_factor]
, wheregenerator
is an existing PennyLane operation class or \(2\times 2\) Hermitian array that acts as the generator of the current operationscaling_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
¶ Gradient computation method.
'A'
: analytic differentiation using the parametershift method.'F'
: finite difference numerical differentiation.None
: the operation may not be differentiated.
Default is
'F'
, orNone
if the Operation has zero parameters.

grad_recipe
= None¶ Gradient recipe for the parametershift 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

inverse
¶ Boolean determining if the inverse of the operation was requested.

matrix
¶

name
¶ Get and set the name of the operator.

num_params
= 1¶

num_wires
= 1¶

par_domain
= 'A'¶

parameters
¶ Current parameter values.

string_for_inverse
= '.inv'¶
Methods
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
()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

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
multiplier, shift
 Return type
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
()¶ Append the operator to the Operator queue.
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