TensoredFilter
class TensoredFilter(cal_matrices, substate_labels_list, mit_pattern)
Bases: object
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)
Methods
apply
TensoredFilter.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.
compute_index_of_cal_mat
TensoredFilter.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
TensoredFilter.flip_state(state, mat_index, flip_poses)
Flip the state according to the chosen qubit positions
Return type
str
Attributes
cal_matrices
Return cal_matrices.
nqubits
Return the number of qubits. See also MeasurementFilter.apply()
qubit_list_sizes
Return _qubit_list_sizes.
substate_labels_list
Return _substate_labels_list