qml.templates.layers.SimplifiedTwoDesign¶

class
SimplifiedTwoDesign
(initial_layer_weights, weights, wires, do_queue=True, id=None)[source]¶ Bases:
pennylane.operation.Operation
Layers consisting of a simplified 2design architecture of PauliY rotations and controlledZ entanglers proposed in Cerezo et al. (2020).
A 2design is an ensemble of unitaries whose statistical properties are the same as sampling random unitaries with respect to the Haar measure up to the first 2 moments.
The template is not a strict 2design, since it does not consist of universal 2qubit gates as building blocks, but has been shown in Cerezo et al. (2020) to exhibit important properties to study “barren plateaus” in quantum optimization landscapes.
The template starts with an initial layer of single qubit PauliY rotations, before the main \(L\) layers are applied. The basic building block of the main layers are controlledZ entanglers followed by a pair of PauliY rotation gates (one for each wire). Each layer consists of an “even” part whose entanglers start with the first qubit, and an “odd” part that starts with the second qubit.
This is an example of two layers, including the initial layer:
The argument
initial_layer_weights
contains the rotation angles of the initial layer of PauliY rotations, whileweights
contains the pairs of PauliY rotation angles of the respective layers. Each layer takes \(\lfloor M/2 \rfloor + \lfloor (M1)/2 \rfloor = M1\) pairs of angles, where \(M\) is the number of wires. The number of layers \(L\) is derived from the first dimension ofweights
. Parameters
initial_layer_weights (tensor_like) – weight tensor for the initial rotation block, shape
(M,)
weights (tensor_like) – tensor of rotation angles for the layers, shape
(L, M1, 2)
wires (Iterable) – wires that the template acts on
Usage Details
template  here shown for two layers  is used inside a
QNode
:import pennylane as qml from pennylane.templates import SimplifiedTwoDesign from math import pi n_wires = 3 dev = qml.device('default.qubit', wires=n_wires) @qml.qnode(dev) def circuit(init_weights, weights): SimplifiedTwoDesign(initial_layer_weights=init_weights, weights=weights, wires=range(n_wires)) return [qml.expval(qml.PauliZ(wires=i)) for i in range(n_wires)] init_weights = [pi, pi, pi] weights_layer1 = [[0., pi], [0., pi]] weights_layer2 = [[pi, 0.], [pi, 0.]] weights = [weights_layer1, weights_layer2] >>> circuit(init_weights, weights) [1., 1., 1.]
Parameter shapes
A list of shapes for the two weights arguments can be computed with the static method
shape()
and used when creating randomly initialised weight tensors:shapes = SimplifiedTwoDesign.shape(n_layers=2, n_wires=2) weights = [np.random.random(size=shape) for shape in shapes]
Attributes
Get base name of the operator.
The basis of an operation, or for controlled gates, of the target operation.
For operations that are controlled, returns the set of control wires.
Eigenvalues of an instantiated operator.
Generator of the operation.
Gradient computation method.
Gradient recipe for the parametershift method.
returns an integer hash uniquely representing the operator
String for the ID of the operator.
Boolean determining if the inverse of the operation was requested.
True
if composing multiple copies of the operation results in an addition (or alternative accumulation) of parameters.True
if the operation is its own inverse.True
if the operation is the same if you exchange the order of wires.True
if the operation is the same if you exchange the order of all but the last wire.Matrix representation of an instantiated operator in the computational basis.
Get and set the name of the operator.
Current parameter values.
The parameters required to implement a singlequbit gate as an equivalent
Rot
gate, up to a global phase.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
andCNOT
havebasis = "X"
, whereasControlledPhaseShift
andRZ
havebasis = "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

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

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. Applyingqml.RZ(0.1, wires=0)
followed byqml.RZ(0.2, wires=0)
is equivalent to performing a single rotationqml.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 asqml.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 asqml.Toffoli(wires=[1, 0, 2])
, but neither are the same asqml.Toffoli(wires=[0, 2, 1])
.If
None
, all instances of the operation will be ignored during compilation transforms that check for controlwire symmetry. Type
bool or None

matrix
¶

name
¶ Get and set the name of the operator.

num_params
= 2¶

num_wires
= 1¶

par_domain
= 'A'¶

parameters
¶ Current parameter values.

single_qubit_rot_angles
¶ The parameters required to implement a singlequbit 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'¶
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
([context])Append the operator to the Operator queue.
shape
(n_layers, n_wires)Returns a list of shapes for the 2 parameter tensors.

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
(context=<class 'pennylane.queuing.QueuingContext'>)¶ Append the operator to the Operator queue.
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