qiskit.ignis.mitigation.TensoredFilter

class TensoredFilter(cal_matrices, substate_labels_list, mit_pattern)

Tensored measurement error mitigation filter.

Produced from a tensored measurement calibration fitter and can be applied to data.

Initialize a tensored measurement error mitigation filter using the cal_matrices from a tensored measurement calibration fitter. A simple usage this class is explained [here] (https://qiskit.org/documentation/tutorials/noise/3_measurement_error_mitigation.html).

Parameters

• cal_matrices (matrix) – the calibration matrices for applying the correction.

• substate_labels_list (list) – for each calibration matrix a list of the states (as strings, states in the subspace)

• mit_pattern (list) – for each calibration matrix a list of the logical qubit indices (as int, states in the subspace)

__init__(cal_matrices, substate_labels_list, mit_pattern)

Initialize a tensored measurement error mitigation filter using the cal_matrices from a tensored measurement calibration fitter. A simple usage this class is explained [here] (https://qiskit.org/documentation/tutorials/noise/3_measurement_error_mitigation.html).

Parameters

• cal_matrices (matrix) – the calibration matrices for applying the correction.

• substate_labels_list (list) – for each calibration matrix a list of the states (as strings, states in the subspace)

• mit_pattern (list) – for each calibration matrix a list of the logical qubit indices (as int, states in the subspace)

Methods

__init__(cal_matrices, substate_labels_list, …)	Initialize a tensored measurement error mitigation filter using the cal_matrices from a tensored measurement calibration fitter.
apply(raw_data[, method, meas_layout])	Apply the calibration matrices to results.
compute_index_of_cal_mat(state, pos_qubits, …)	Return the index of (pseudo inverse) calibration matrix for the input quantum state
flip_state(state, mat_index, flip_poses)	Flip the state according to the chosen qubit positions

Attributes

cal_matrices	Return cal_matrices.
nqubits	Return the number of qubits.
qubit_list_sizes	Return _qubit_list_sizes.
substate_labels_list	Return _substate_labels_list

apply(raw_data, method='least_squares', meas_layout=None)

Apply the calibration matrices to results.

Parameters

• raw_data (dict or Result) –

  The data to be corrected. Can be in one of two forms:

  • A counts dictionary from results.get_counts

  • A Qiskit Result

• method (str) –

  fitting method. The following methods are supported:

  • ’pseudo_inverse’: direct inversion of the cal matrices.

    Mitigated counts can contain negative values and the sum of counts would not equal to the shots. Mitigation is conducted qubit wise: For each qubit, mitigate the whole counts using the calibration matrices which affect the corresponding qubit. For example, assume we are mitigating the 3rd bit of the 4-bit counts using ‘2 imes 2’ calibration matrix A_3. When mitigating the count of ‘0110’ in this step, the following formula is applied: count[‘0110’] = A_3^{-1}[1, 0]*count[‘0100’] + A_3^{-1}[1, 1]*count[‘0110’].

    The total time complexity of this method is O(m2^{n + t}), where n is the size of calibrated qubits, m is the number of sets in mit_pattern, and t is the size of largest set of mit_pattern. If the mit_pattern is shaped like [[0], [1], [2], …, [n-1]], which corresponds to the tensor product noise model without cross-talk, then the time complexity would be O(n2^n). If the mit_pattern is shaped like [[0, 1, 2, …, n-1]], which exactly corresponds to the complete error mitigation, then the time complexity would be O(2^(n+n)) = O(4^n).

  • ’least_squares’: constrained to have physical probabilities.

    Instead of directly applying inverse calibration matrices, this method solve a constrained optimization problem to find the closest probability vector to the result from ‘pseudo_inverse’ method. Sequential least square quadratic programming (SLSQP) is used in the internal process. Every updating step in SLSQP takes O(m2^{n+t}) time. Since this method is using the SLSQP optimization over the vector with lenght 2^n, the mitigation for 8 bit counts with the mit_pattern = [[0], [1], [2], …, [n-1]] would take 10 seconds or more.

  • If None, ‘least_squares’ is used.

• meas_layout (list of int) –

  the mapping from classical registers to qubits

  • If you measure qubit 2 to clbit 0, 0 to 1, and 1 to 2,

    the list becomes [2, 0, 1]

  • If None, flatten(mit_pattern) is used.

Returns

The corrected data in the same form as raw_data

Return type

dict or Result

Raises

QiskitError – if raw_data is not in a one of the defined forms.

property cal_matrices

Return cal_matrices.

compute_index_of_cal_mat(state, pos_qubits, indices)

Return the index of (pseudo inverse) calibration matrix for the input quantum state

Return type

int

flip_state(state, mat_index, flip_poses)

Flip the state according to the chosen qubit positions

Return type

str

property nqubits

Return the number of qubits. See also MeasurementFilter.apply()

property qubit_list_sizes

Return _qubit_list_sizes.

property substate_labels_list

Return _substate_labels_list