qml.templates.layers.BasicEntanglerLayers¶

class
BasicEntanglerLayers
(weights, wires=None, rotation=None, do_queue=True)[source]¶ Bases:
pennylane.operation.Operation
Layers consisting of oneparameter singlequbit rotations on each qubit, followed by a closed chain or ring of CNOT gates.
The ring of CNOT gates connects every qubit with its neighbour, with the last qubit being considered as a neighbour to the first qubit.
The number of layers \(L\) is determined by the first dimension of the argument
weights
. When using a single wire, the template only applies the single qubit gates in each layer.Note
This template follows the convention of dropping the entanglement between the last and the first qubit when using only two wires, so the entangler is not repeated on the same wires. In this case, only one CNOT gate is applied in each layer:
 Parameters
weights (tensor_like) – Weight tensor of shape
(L, len(wires))
. Each weight is used as a parameter for the rotation.wires (Iterable) – wires that the template acts on
rotation (pennylane.ops.Operation) – oneparameter singlequbit gate to use, if
None
,RX
is used as default
 Raises
ValueError – if inputs do not have the correct format
Usage Details
The template is used inside a qnode:
import pennylane as qml from pennylane.templates import BasicEntanglerLayers from math import pi n_wires = 3 dev = qml.device('default.qubit', wires=n_wires) @qml.qnode(dev) def circuit(weights): BasicEntanglerLayers(weights=weights, wires=range(n_wires)) return [qml.expval(qml.PauliZ(wires=i)) for i in range(n_wires)]
>>> circuit([[pi, pi, pi]]) [1., 1., 1.]
Parameter shape
The shape of the weights argument can be computed by the static method
shape()
and used when creating randomly initialised weight tensors:shape = BasicEntanglerLayers.shape(n_layers=2, n_wires=2) weights = np.random.random(size=shape)
No periodic boundary for two wires
When using two wires, the convention is to drop the periodic boundary condition. This means that the connection from the second to the first wire is omitted.
n_wires = 2 dev = qml.device('default.qubit', wires=n_wires) @qml.qnode(dev) def circuit(weights): BasicEntanglerLayers(weights=weights, wires=range(n_wires)) return [qml.expval(qml.PauliZ(wires=i)) for i in range(n_wires)]
>>> circuit([[pi, pi]]) [1, 1]
Changing the rotation gate
Any singlequbit gate can be used as a rotation gate, as long as it only takes a single parameter. The default is the
RX
gate.@qml.qnode(dev) def circuit(weights): BasicEntanglerLayers(weights=weights, wires=range(n_wires), rotation=qml.RZ) return [qml.expval(qml.PauliZ(wires=i)) for i in range(n_wires)]
Accidentally using a gate that expects more parameters throws a
ValueError: Wrong number of parameters
.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_wires)Returns the shape of the weight tensor required for this template.

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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