# qml.templates.layers.SimplifiedTwoDesign¶

class SimplifiedTwoDesign(initial_layer_weights, weights, wires, do_queue=True, id=None)[source]

Layers consisting of a simplified 2-design architecture of Pauli-Y rotations and controlled-Z entanglers proposed in Cerezo et al. (2020).

A 2-design 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 2-design, since it does not consist of universal 2-qubit 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 Pauli-Y rotations, before the main $$L$$ layers are applied. The basic building block of the main layers are controlled-Z entanglers followed by a pair of Pauli-Y 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 Pauli-Y rotations, while weights contains the pairs of Pauli-Y rotation angles of the respective layers. Each layer takes $$\lfloor M/2 \rfloor + \lfloor (M-1)/2 \rfloor = M-1$$ pairs of angles, where $$M$$ is the number of wires. The number of layers $$L$$ is derived from the first dimension of weights.

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, M-1, 2)

• wires (Iterable) – wires that the template acts on

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]

 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 Gradient computation 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

• 'A': analytic differentiation using the parameter-shift method.

• 'F': finite difference numerical differentiation.

• None: the operation may not be differentiated.

Default is 'F', or None if the Operation has zero parameters.

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 = 2
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. 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. 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

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_wires)[source]

Returns a list of shapes for the 2 parameter tensors.

Parameters
• n_layers (int) – number of layers

• n_wires (int) – number of wires

Returns

list of shapes

Return type

list[tuple[int]]