qiskit_noise_learning.analysis.LinearSystemData

class qiskit_noise_learning.analysis.LinearSystemData(A: ndarray, b: ndarray, sigma_b: ndarray, row_diagnostics: Mapping[str, ndarray], row_index_map: Mapping[RowIndex, int], column_index_map: Mapping[ColumnIndex, int], time_lb: datetime64, time_ub: datetime64)

Bases: Generic[RowIndex, ColumnIndex]

The linear system to solve and metadata in raw format.

A linear system A @ x = b with axis labels and metadata.

The row and column labels are of arbitrary hashable types: this class carries no assumptions about what a row or column denotes. In the systems built by from_fit() the rows are Path objects and the columns are the fidelity model’s parameter labels.

The index maps are the authoritative record of how labels correspond to positions in A. Anything that needs to align a label-keyed quantity with the arrays should index through row_index_map or column_index_map rather than rebuilding the correspondence from row_labels or column_labels.

Parameters

• A – The matrix with shape (m, n).
• b – The target vector length m.
• sigma_b – Statistical 1-sigma uncertainty on b per row, with length m.
• row_diagnostics – Named per-row quantities recorded by the stages that produced b, each an array of length m holding nan for rows the quantity is undefined for. Keys are the metadata names used upstream; see from_fit().
• row_index_map – A mapping from row labels to their integer row position in A.
• column_index_map – A mapping from column labels to their integer column position in A.
• time_lb – Earliest time bound across the rows.
• time_ub – Latest time bound across the rows.

__init__

Methods

Attributes

row_labels

column_labels

from_fit