qml.templates.layers.StronglyEntanglingLayers¶

class
StronglyEntanglingLayers
(weights, wires, ranges=None, imprimitive=None, do_queue=True)[source]¶ Bases:
pennylane.operation.Operation
Layers consisting of single qubit rotations and entanglers, inspired by the circuitcentric classifier design arXiv:1804.00633.
The argument
weights
contains the weights for each layer. The number of layers \(L\) is therefore derived from the first dimension ofweights
.The 2qubit gates, whose type is specified by the
imprimitive
argument, act chronologically on the \(M\) wires, \(i = 1,...,M\). The second qubit of each gate is given by \((i+r)\mod M\), where \(r\) is a hyperparameter called the range, and \(0 < r < M\). If applied to one qubit only, this template will use no imprimitive gates.This is an example of two 4qubit strongly entangling layers (ranges \(r=1\) and \(r=2\), respectively) with rotations \(R\) and CNOTs as imprimitives:
Note
The twoqubit gate used as the imprimitive or entangler must not depend on parameters.
 Parameters
weights (tensor_like) – weight tensor of shape
(L, M, 3)
wires (Iterable) – wires that the template acts on
ranges (Sequence[int]) – sequence determining the range hyperparameter for each subsequent layer; if None using \(r=l \mod M\) for the \(l\) th layer and \(M\) wires.
imprimitive (pennylane.ops.Operation) – twoqubit gate to use, defaults to
CNOT
Usage Details
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 = StronglyEntanglingLayers.shape(n_layers=2, n_wires=2) 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.
String for the ID of the operator.
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

id
¶ String for the ID of the operator.

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_wires)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
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
()¶ Append the operator to the Operator queue.
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