qml.operation

This module contains the abstract base classes for defining PennyLane operations and observables.

Warning

Unless you are a PennyLane or plugin developer, you likely do not need to use these classes directly.

See the main operations page for details on available operations and observables.

Description

Qubit Operations

The Operator class serves as a base class for operators, and is inherited by the Operation class. These classes are subclassed to implement quantum operations and measure observables in PennyLane.

• Each Operator subclass represents a general type of map between physical states. Each instance of these subclasses represents either

  • an application of the operator or

  • an instruction to measure and return the respective result.

  Operators act on a sequence of wires (subsystems) using given parameter values.

• Each Operation subclass represents a type of quantum operation, for example a unitary quantum gate. Each instance of these subclasses represents an application of the operation with given parameter values to a given sequence of wires (subsystems).

Differentiation

In general, an Operation is differentiable (at least using the finite-difference method) with respect to a parameter iff

• the domain of that parameter is continuous.

For an Operation to be differentiable with respect to a parameter using the analytic method of differentiation, it must satisfy an additional constraint:

• the parameter domain must be real.

Note

These conditions are not sufficient for analytic differentiation. For example, CV gates must also define a matrix representing their Heisenberg linear transformation on the quadrature operators.

CV Operation base classes

Due to additional requirements, continuous-variable (CV) operations must subclass the CVOperation or CVObservable classes instead of Operation.

Differentiation

To enable gradient computation using the analytic method for Gaussian CV operations, in addition, you need to provide the static class method _heisenberg_rep() that returns the Heisenberg representation of the operation given its list of parameters, namely:

• For Gaussian CV Operations this method should return the matrix of the linear transformation carried out by the operation on the vector of quadrature operators \(\mathbf{r}\) for the given parameter values.

• For Gaussian CV Observables this method should return a real vector (first-order observables) or symmetric matrix (second-order observables) of coefficients of the quadrature operators \(\x\) and \(\p\).

PennyLane uses the convention \(\mathbf{r} = (\I, \x, \p)\) for single-mode operations and observables and \(\mathbf{r} = (\I, \x_0, \p_0, \x_1, \p_1, \ldots)\) for multi-mode operations and observables.

Note

Non-Gaussian CV operations and observables are currently only supported via the finite-difference method of gradient computation.

Contents

Operator Types

Operator(*params[, wires, id])	Base class representing quantum operators.
Operation(*params[, wires, id])	Base class representing quantum gates or channels applied to quantum states.
CV()	A mixin base class denoting a continuous-variable operation.
CVObservable(*params[, wires, id])	Base class representing continuous-variable observables.
CVOperation(*params[, wires, id])	Base class representing continuous-variable quantum gates.
Channel(*params[, wires, id])	Base class for quantum channels.
StatePrepBase(*params[, wires, id])	An interface for state-prep operations.

Boolean Functions

BooleanFn’s are functions of a single object that return True or False. The operation module provides the following:

is_trainable

Returns True if any of the parameters of an operator is trainable according to qml.math.requires_grad.

Other

operation_derivative(operation)

Calculate the derivative of an operation.

PennyLane also provides a function for checking the consistency and correctness of an operator instance.

assert_valid(op, *[, skip_deepcopy, ...])

Runs basic validation checks on an Operator to make sure it has been correctly defined.

Operation attributes

PennyLane contains a mechanism for storing lists of operations with similar attributes and behaviour (for example, those that are their own inverses). The attributes below are already included, and are used primarily for the purpose of compilation transforms. New attributes can be added by instantiating new Attribute objects. Please note that these objects are located in pennylane.ops.qubit.attributes, not pennylane.operation.

Attribute	Class to represent a set of operators with a certain attribute.
composable_rotations	Operations for which composing multiple copies of the operation results in an addition (or alternative accumulation) of parameters.
diagonal_in_z_basis	Operations that are diagonal in the computational basis.
has_unitary_generator	Operations that are generated by a unitary operator.
self_inverses	Operations that are their own inverses.
supports_broadcasting	Operations that support parameter broadcasting.
symmetric_over_all_wires	Operations that are the same if you exchange the order of wires.
symmetric_over_control_wires	Controlled operations that are the same if you exchange the order of all but the last (target) wire.

code/qml_operation

 

Download Python script

 

Download Notebook

 

View on GitHub