Process Tomography¶

class lightworks.tomography.LIProcessTomography(n_qubits: int, base_circuit: PhotonicCircuit)¶

Bases: _ProcessTomography

Runs quantum process tomography using the linear inversion estimation method.

Parameters:

n_qubits (int) – The number of qubits that will be used as part of the tomography.
base_circuit (PhotonicCircuit) – An initial circuit which realises the required operation and can be modified for performing tomography. It is required that the number of circuit input modes equals 2 * the number of qubits.

property base_circuit: PhotonicCircuit¶: The base circuit which the tomography experiment will be performed on.

property choi: ndarray[tuple[int, ...], dtype[complex128]]¶: Returns the calculate choi matrix for a circuit.

fidelity(choi_exp: ndarray[tuple[int, ...], dtype[complex128]]) → float¶: Calculates fidelity of the calculated choi matrix compared to the expected one.

get_experiments() → ProcessTomographyList¶: Generates all required tomography experiments for performing a process tomography algorithm.

property n_qubits: int¶: The number of qubits within the system.

process(data: list[dict[State, int]] | dict[tuple[str, str], dict[State, int]]) → ndarray[tuple[int, ...], dtype[complex128]]¶

Performs process tomography with the configured elements and calculates the choi matrix using linear inversion.

Parameters:: data (list | dict) – The collected measurement data. If a list then this should match the order the experiments were provided, and if a dictionary, then each key should be tuple of the input and measurement basis.
Returns:: The calculated choi matrix for the process.
Return type:: np.ndarray

class lightworks.tomography.MLEProcessTomography(n_qubits: int, base_circuit: PhotonicCircuit)¶

Bases: _ProcessTomography

Runs quantum process tomography using the maximum likelihood estimation method.

Parameters:

n_qubits (int) – The number of qubits that will be used as part of the tomography.
base_circuit (PhotonicCircuit) – An initial circuit which realises the required operation and can be modified for performing tomography. It is required that the number of circuit input modes equals 2 * the number of qubits.

property base_circuit: PhotonicCircuit¶: The base circuit which the tomography experiment will be performed on.

property choi: ndarray[tuple[int, ...], dtype[complex128]]¶: Returns the calculate choi matrix for a circuit.

fidelity(choi_exp: ndarray[tuple[int, ...], dtype[complex128]]) → float¶: Calculates fidelity of the calculated choi matrix compared to the expected one.

get_experiments() → ProcessTomographyList¶: Generates all required tomography experiments for performing a process tomography algorithm.

property n_qubits: int¶: The number of qubits within the system.

process(data: list[dict[State, int]] | dict[tuple[str, str], dict[State, int]]) → ndarray[tuple[int, ...], dtype[complex128]]¶

Performs process tomography with the configured elements and calculates the choi matrix using maximum likelihood estimation.

Parameters:: data (list | dict) – The collected measurement data. If a list then this should match the order the experiments were provided, and if a dictionary, then each key should be tuple of the input and measurement basis.
Returns:: The calculated choi matrix for the process.
Return type:: np.ndarray

class lightworks.tomography.process_tomography_mle.MLETomographyAlgorithm(n_qubits: int, input_basis: list[str])¶

Bases: object

Implements the pgdB algorithm for maximum likelihood estimation from https://arxiv.org/abs/1803.10062 for calculation of a physical choi matrix from the tomography measurement data.

Parameters:: n_qubits (int) – The number of qubits used within the tomography.

pgdb(data: dict[tuple[str, str], float], max_iter: int = 1000, stop_threshold: float = 1e-10) → ndarray[tuple[int, ...], dtype[complex128]]¶

Runs the pgdB algorithm on the provided data set.

Parameters:

data (dict) – The measured tomography experiment data. The keys of this dictionary should be the input/measurement basis and the values should be the calculated expectation values.
max_iter (int, optional) – Sets the maximum number of iterations that the algorithm can do, defaults to 1000.
stop_threshold (float, optional) – Sets the stopping threshold for the gradient descent algorithm. Defaults to 1e-10.

Returns:

The calculated choi matrix

Return type:

np.ndarray