qml.operation.Operation¶
-
class
Operation
(*params, wires=None, do_queue=True)[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.
Finally, there are some additional optional class attributes that may be set, and used by certain quantum optimizers:
- Parameters
params (tuple[float, int, array, Variable]) – 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 operator.
Generator of the operation.
Gradient computation method.
Gradient recipe for the parameter-shift method.
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.
-
do_check_domain
= True¶
-
eigvals
¶ Eigenvalues of an instantiated operator.
Note that the eigenvalues are not guaranteed to be in any particular order.
Example:
>>> U = qml.RZ(0.5, wires=1) >>> U.eigvals >>> array([0.96891242-0.24740396j, 0.96891242+0.24740396j])
- Returns
eigvals representation
- Return type
array
-
generator
¶ Generator of the operation.
A length-2 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 parameter-shift 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 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
-
inverse
¶ Boolean determining if the inverse of the operation was requested.
-
matrix
¶ Matrix representation of an instantiated operator in the computational basis.
Example:
>>> U = qml.RY(0.5, wires=1) >>> U.matrix >>> array([[ 0.96891242+0.j, -0.24740396+0.j], [ 0.24740396+0.j, 0.96891242+0.j]])
- Returns
matrix representation
- Return type
array
-
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.
Fixed parameters are returned as is, free parameters represented by
Variable
instances are replaced by their current numerical value.- Returns
parameter values
- Return type
list[Any]
-
string_for_inverse
= '.inv'¶
Methods
check_domain
(p[, flattened])Check the validity of a parameter.
decomposition
(*params, wires)Returns a template decomposing the operation into other quantum operations.
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.
-
check_domain
(p, flattened=False)¶ Check the validity of a parameter.
Variable
instances can represent any real scalars (but not arrays).- Parameters
p (Number, array, Variable) – parameter to check
flattened (bool) – True means p is an element of a flattened parameter sequence (affects the handling of ‘A’ parameters)
- Raises
TypeError – parameter is not an element of the expected domain
ValueError – parameter is an element of an unknown domain
- Returns
p
- Return type
Number, array, Variable
-
static
decomposition
(*params, wires)[source]¶ Returns a template decomposing the operation into other quantum operations.
-
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
multiplier, shift
- Return type
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
-
queue
()¶ Append the operator to the Operator queue.
Contents
Using PennyLane
Development
API
Downloads