hybridlane.templates.fock_state.FockState

class hybridlane.templates.fock_state.FockState(n, wires=None, id=None)

Bases: pennylane.ops.Operation, hybridlane.ops.Hybrid

Prepares a definite Fock state from the vacuum

Unlike PennyLane’s FockState, this class uses a sequence of Red and Blue gates, requiring an ancilla qubit.

Details:

• Number of wires: 2

• Wire arguments: [qubit, qumode]

• Number of parameters: 1

• Number of dimensions per parameter: (0,)

This prepares a definite Fock state on a qumode using a sequence of red and blue sideband gates, favoring the Sideband ISA [1]. The gate sequence to prepare Fock state \(\ket{n}\) is given by

\[X^{n\mod 2} JC(\frac{\pi}{2\sqrt{n+1}}, \frac{\pi}{2}) AJC(\frac{\pi}{2\sqrt{n}}, \frac{\pi}{2}) \dots JC(\frac{\pi}{2\sqrt{2}}, \frac{\pi}{2}) AJC(\frac{\pi}{2}, \frac{\pi}{2})\]

The final \(X\) gate is applied if \(n\) is odd to uncompute the qubit.

This also provides a decomposition for PennyLane’s FockState that uses an ancilla qubit to prepare the Fock state on the qumode, requiring dynamic qubit allocation.

References

[1]

Yuan Liu, Shraddha Singh, Kevin C Smith, Eleanor Crane, John M Martyn, Alec Eickbusch, Alexander Schuckert, Richard D Li, Jasmine Sinanan-Singh, Micheline B Soley, and others. Hybrid oscillator-qubit quantum processors: instruction set architectures, abstract machine models, and applications. PRX Quantum, 7(1):010201, 2026.

Parameters:

• n (int)

• wires (pennylane.wires.WiresLike)

• id (str | None)

num_params = 1

Number of trainable parameters that the operator depends on.

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 = 2

Number of wires the operator acts on.

num_qumodes = 1

The number of qumodes the gate acts on

grad_method = None

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.

resource_keys

The set of parameters that affects the resource requirement of the operator.

All decomposition rules for this operator class are expected to have a resource function that accepts keyword arguments that match these keys exactly. The resource_rep() function will also expect keyword arguments that match these keys when called with this operator type.

The default implementation is an empty set, which is suitable for most operators.

See also

resource_params()

property resource_params

A dictionary containing the minimal information needed to compute a resource estimate of the operator’s decomposition.

The keys of this dictionary should match the resource_keys attribute of the operator class. Two instances of the same operator type should have identical resource_params iff their decompositions exhibit the same counts for each gate type, even if the individual gate parameters differ.

Examples

The MultiRZ has non-empty resource_keys:

>>> qml.MultiRZ.resource_keys
{'num_wires'}

The resource_params of an instance of MultiRZ will contain the number of wires:

>>> op = qml.MultiRZ(0.5, wires=[0, 1])
>>> op.resource_params
{'num_wires': 2}

Note that another MultiRZ may have different parameters but the same resource_params:

>>> op2 = qml.MultiRZ(0.7, wires=[1, 2])
>>> op2.resource_params
{'num_wires': 2}