qml.operation.Operation¶

class Operation(*params, wires=None, do_queue=True, id=None)[source]¶

Bases: pennylane.operation.Operator

Base class for quantum operations supported by a device.

As with Operator, the following class attributes must be defined for all operations:

The following two class attributes are optional, but in most cases should be clearly defined to avoid unexpected behavior during differentiation.

grad_method
grad_recipe

Finally, there are some additional optional class attributes that may be set, and used by certain quantum optimizers:

generator

Parameters

params (tuple[float, int, array]) – operation parameters

Keyword Arguments

wires (Sequence[int]) – Subsystems it acts on. If not given, args[-1] is interpreted as wires.
do_queue (bool) – Indicates whether the operation should be immediately pushed into a BaseQNode circuit queue. This flag is useful if there is some reason to run an Operation outside of a BaseQNode context.

Attributes

`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`	Number of parameters the operator takes.
`num_wires`	Number of wires the operator acts on.
`par_domain`	Domain of the gate parameters.
`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¶

Gradient computation 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¶: Number of parameters the operator takes.

num_wires¶: Number of wires the operator acts on.

par_domain¶

Domain of the gate parameters.

'N': natural numbers (including zero).
'R': floats.
'A': arrays of real or complex values.
'L': list of arrays of real or complex values.
None: if there are no parameters.

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

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.

adjoint(do_queue=False)[source]¶

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)[source]¶: 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)[source]¶

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()[source]¶

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.

qml.operation.Operation¶

Attributes

Methods

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