pennylane_ionq.ops.YY¶
- class YY(phi, wires)[source]¶
Bases:
pennylane.operation.Operation
The Ising YY gate.
\[YY(\phi) = e^{-\frac{\phi}{2}\hat{Y}\otimes\hat{Y}}.\]Details:
Number of wires: 2
Number of parameters: 1
Gradient recipe: \(\frac{d}{d\phi}f(YY(\phi)) = \frac{1}{2}\left[f(YY(\phi+\pi/2)) - f(YY(\phi-\pi/2))\right]\) where \(f\) is an expectation value depending on \(YY(\phi)\).
- Parameters
phi (float) – rotation angle \(\phi\)
wires (Sequence[int]) – the subsystems the operation acts on
Attributes
Get base name of the operator.
Eigenvalues of an instantiated operator.
Generator of the operation.
Gradient recipe for the parameter-shift 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.
Current parameter values.
Wires of this operator.
- base_name¶
Get base name of the operator.
- eigvals¶
- 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 = 'A'¶
- 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
- 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 = 1¶
- num_wires = 2¶
- par_domain = 'R'¶
- parameters¶
Current parameter values.
- 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
()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
JacobianTape
- 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
Operator
- queue()¶
Append the operator to the Operator queue.
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