qml.operation.DiagonalOperation¶

class
DiagonalOperation
(*params, wires=None, do_queue=True, id=None)[source]¶ Bases:
pennylane.operation.Operation
Base class for diagonal quantum operations supported by a device.
As with
Operation
, 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.
Finally, there are some additional optional class attributes that may be set, and used by certain quantum optimizers:
 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
Get base name of the operator.
Eigenvalues of an instantiated diagonal operation.
Generator of the operation.
Gradient computation method.
Gradient recipe for the parametershift method.
String for the ID of the operator.
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.
Number of parameters the operator takes.
Number of wires the operator acts on.
Domain of the gate parameters.
Current parameter values.
Wires of this operator.

base_name
¶ Get base name of the operator.

eigvals
¶ Eigenvalues of an instantiated diagonal operation.
The order of the eigenvalues needs to match the order of the computational basis vectors.
Example:
>>> U = qml.RZ(0.5, wires=1) >>> U.eigvals >>> array([0.968912420.24740396j, 0.96891242+0.24740396j])
 Returns
eigvals representation
 Return type
array

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

id
¶ String for the ID of the operator.

inverse
¶ Boolean determining if the inverse of the operation was requested.

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.

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.

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
()¶ 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

queue
()¶ Append the operator to the Operator queue.
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