diffqcQubitDevice#

class diffqc.pennylane.diffqcQubitDevice(wires: ~typing.Union[int, ~typing.Iterable[~typing.Union[int, str]]], shots: ~typing.Union[None, int, ~typing.List[int]] = None, mode: ~typing.Literal['dense', 'sparse'] = 'dense', dtype: ~numpy.dtype = <class 'jax.numpy.complex64'>)#

Bases: QubitDevice

Attributes Summary

`author`
`name`
`observables`
`operations`
`pennylane_requires`
`short_name`
`version`

Methods Summary

`analytic_probability`([wires])	Return the (marginal) probability of each computational basis state from the last run of the device.
`apply`(operations, **kwargs)	Apply quantum operations, rotate the circuit into the measurement basis, and compile and execute the quantum circuit.
`capability`()

Attributes Documentation

author = 'ymd-h'#

name = 'PennyLane plugin for diffqc'#

observables = {'PauliX', 'PauliY', 'PauliZ'}#

operations = {'CNOT', 'CPhase', 'CPhaseShift', 'CRX', 'CRY', 'CRZ', 'CRot', 'CSWAP', 'CY', 'CZ', 'ControlledPhaseShift', 'ECR', 'Hadamard', 'ISWAP', 'Identity', 'IsingXX', 'IsingYY', 'IsingZZ', 'PSWAP', 'PauliX', 'PauliY', 'PauliZ', 'PhaseShift', 'RX', 'RY', 'RZ', 'Rot', 'S', 'SISWAP', 'SQISWAP', 'SWAP', 'SX', 'T', 'Toffoli', 'U1', 'U2', 'U3'}#

pennylane_requires = '>=0.20.0'#

short_name = 'diffqc.qubit'#

version = '0.0.0'#

Methods Documentation

analytic_probability(wires: Union[None, Iterable[Union[int, str]], int, str, Wires] = None)#

Return the (marginal) probability of each computational basis state from the last run of the device.

PennyLane uses the convention $| q_{0}, q_{1}, \dots, q_{N - 1} ⟩$ where $q_{0}$ is the most significant bit.

If no wires are specified, then all the basis states representable by the device are considered and no marginalization takes place.

Note

marginal_prob() may be used as a utility method to calculate the marginal probability distribution.

Parameters: wires (Iterable[Number, str], Number, str, Wires) – wires to return marginal probabilities for. Wires not provided are traced out of the system.
Returns: list of the probabilities
Return type: array[float]

apply(operations: List[Operation], **kwargs)#

Apply quantum operations, rotate the circuit into the measurement basis, and compile and execute the quantum circuit.

This method receives a list of quantum operations queued by the QNode, and should be responsible for:

Constructing the quantum program
(Optional) Rotating the quantum circuit using the rotation operations provided. This diagonalizes the circuit so that arbitrary observables can be measured in the computational basis.
Compile the circuit
Execute the quantum circuit

Both arguments are provided as lists of PennyLane Operation instances. Useful properties include name, wires, and parameters, and inverse:

>>> op = qml.RX(0.2, wires=[0])
>>> op.name # returns the operation name
"RX"
>>> op.wires # returns a Wires object representing the wires that the operation acts on
<Wires = [0]>
>>> op.parameters # returns a list of parameters
[0.2]
>>> op.inverse # check if the operation should be inverted
False
>>> op = qml.RX(0.2, wires=[0]).inv
>>> op.inverse
True

Parameters

operations (list[Operation]) – operations to apply to the device

Keyword Arguments

rotations (list[Operation]) – operations that rotate the circuit pre-measurement into the eigenbasis of the observables.
hash (int) – the hash value of the circuit constructed by CircuitGraph.hash

classmethod capability() → Dict[str, Any]#