qml.operation.CVOperation¶

class
CVOperation
(*params, wires=None, do_queue=True, id=None)[source]¶ Bases:
pennylane.operation.CV
,pennylane.operation.Operation
Base class for continuousvariable quantum operations.
Attributes
Get base name of the operator.
The basis of an operation, or for controlled gates, of the target operation.
Returns the control wires.
Eigenvalues of an instantiated operator.
Generator of the operation.
Gradient computation method.
Gradient recipe for the parametershift method.
returns an integer hash uniquely representing the operator
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 trainable parameters that this operator expects to be fed via the dynamic *params argument.
Number of wires the operator acts on.
Current parameter values.
The parameters required to implement a singlequbit gate as an equivalent
Rot
gate, up to a global phase.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
andCNOT
havebasis = "X"
, whereasControlledPhaseShift
andRZ
havebasis = "Z"
. Type
str or None

control_wires
¶ Returns the control wires. For operations that are not controlled, this is an empty
Wires
object of length0
. Returns
The control wires of the operation.
 Return type

eigvals
¶

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

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.

matrix
¶

name
¶ Get and set the name of the operator.

num_params
¶ Number of trainable parameters that this operator expects to be fed via the dynamic *params argument.
By default, this property returns as many parameters as were used for the operator creation. If the number of parameters for an operator subclass is fixed, this property can be overwritten to return the fixed value.
 Returns
number of parameters
 Return type
int

num_wires
¶ Number of wires the operator acts on.

parameters
¶ Current parameter values.

single_qubit_rot_angles
¶ The parameters required to implement a singlequbit 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'¶

supports_heisenberg
= False¶

supports_parameter_shift
= False¶
Methods
adjoint
([do_queue])Create an operation that is the adjoint of this one.
Decomposes this operator into products of other operators.
decomposition
(*params, wires)Defines a decomposition of this operator into products of other operators.
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.
heisenberg_expand
(U, wires)Expand the given local Heisenbergpicture array into a fullsystem one.
heisenberg_pd
(idx)Partial derivative of the Heisenberg picture transform matrix.
heisenberg_tr
(wires[, inverse])Heisenberg picture representation of the linear transformation carried out by the gate at current parameter values.
inv
()Inverts the operation, such that the inverse will be used for the computations by the specific device.
label
([decimals, base_label])A customizable string representation of the operator.
queue
([context])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.

decompose
()¶ Decomposes this operator into products of other operators.
 Returns
list[Operation]

static
decomposition
(*params, wires)¶ Defines a decomposition of this operator into products of other operators.
 Parameters
params (tuple[float, int, array]) – operator parameters
wires (Union(Sequence[int], Wires)) – wires the operator acts on
 Returns
list[Operation]

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]]

heisenberg_expand
(U, wires)¶ Expand the given local Heisenbergpicture array into a fullsystem one.
 Parameters
U (array[float]) – array to expand (expected to be of the dimension
1+2*self.num_wires
)wires (Wires) – wires on the device the array
U
should be expanded to apply to
 Raises
ValueError – if the size of the input matrix is invalid or num_wires is incorrect
 Returns
expanded array, dimension
1+2*num_wires
 Return type
array[float]

heisenberg_pd
(idx)[source]¶ Partial derivative of the Heisenberg picture transform matrix.
Computed using grad_recipe.
 Parameters
idx (int) – index of the parameter with respect to which the partial derivative is computed.
 Returns
partial derivative
 Return type
array[float]

heisenberg_tr
(wires, inverse=False)[source]¶ Heisenberg picture representation of the linear transformation carried out by the gate at current parameter values.
Given a unitary quantum gate \(U\), we may consider its linear transformation in the Heisenberg picture, \(U^\dagger(\cdot) U\).
If the gate is Gaussian, this linear transformation preserves the polynomial order of any observables that are polynomials in \(\mathbf{r} = (\I, \x_0, \p_0, \x_1, \p_1, \ldots)\). This also means it maps \(\text{span}(\mathbf{r})\) into itself:
\[U^\dagger \mathbf{r}_i U = \sum_j \tilde{U}_{ij} \mathbf{r}_j\]For Gaussian CV gates, this method returns the transformation matrix for the current parameter values of the Operation. The method is not defined for nonGaussian (and nonCV) gates.
 Parameters
wires (Wires) – wires on the device that the observable gets applied to
inverse (bool) – if True, return the inverse transformation instead
 Raises
RuntimeError – if the specified operation is not Gaussian or is missing the _heisenberg_rep method
 Returns
\(\tilde{U}\), the Heisenberg picture representation of the linear transformation
 Return type
array[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

label
(decimals=None, base_label=None)¶ A customizable string representation of the operator.
 Parameters
decimals=None (int) – If
None
, no parameters are included. Else, specifies how to round the parameters.base_label=None (str) – overwrite the nonparameter component of the label
 Returns
label to use in drawings
 Return type
str
Example:
>>> op = qml.RX(1.23456, wires=0) >>> op.label() "RX" >>> op.label(decimals=2) "RX\n(1.23)" >>> op.label(base_label="my_label") "my_label" >>> op.label(decimals=2, base_label="my_label") "my_label\n(1.23)" >>> op.inv() >>> op.label() "RX⁻¹"

queue
(context=<class 'pennylane.queuing.QueuingContext'>)¶ Append the operator to the Operator queue.

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