---
title: analysis (latest version)
description: API reference for qiskit_noise_learning.analysis in the latest version of qiskit-noise-learning
source: https://quantum.cloud.ibm.com/docs/en/api/qiskit-noise-learning/analysis
---

# Analysis

`qiskit_noise_learning.analysis`

Data analysis.

## Classes

|                                                                                                                                                          |                                                                                                                                                                                  |
| -------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [`AnalysisPipeline`](/docs/api/qiskit-noise-learning/generated/analysis-analysis-pipeline "qiskit_noise_learning.analysis.AnalysisPipeline")             | A composite [`AnalysisStage`](/docs/api/qiskit-noise-learning/generated/analysis-analysis-stage "qiskit_noise_learning.analysis.AnalysisStage") that chains stages sequentially. |
| [`AnalysisStage`](/docs/api/qiskit-noise-learning/generated/analysis-analysis-stage "qiskit_noise_learning.analysis.AnalysisStage")                      | Abstract base for a stage in the analysis pipeline.                                                                                                                              |
| [`AverageObservables`](/docs/api/qiskit-noise-learning/generated/analysis-average-observables "qiskit_noise_learning.analysis.AverageObservables")       | Average observables over randomizations for each unbound path and fragment depth pair.                                                                                           |
| [`ComputeObservables`](/docs/api/qiskit-noise-learning/generated/analysis-compute-observables "qiskit_noise_learning.analysis.ComputeObservables")       | Compute observable data from raw data.                                                                                                                                           |
| [`CurveFitObservables`](/docs/api/qiskit-noise-learning/generated/analysis-curve-fit-observables "qiskit_noise_learning.analysis.CurveFitObservables")   | Fit observable data to exponential decays of the form `a * f**fragment_depth`, and average any remaining observables over randomizations.                                        |
| [`Fit`](/docs/api/qiskit-noise-learning/generated/analysis-fit "qiskit_noise_learning.analysis.Fit")                                                     | Container for data at each level of the analysis hierarchy.                                                                                                                      |
| [`FlipPostSelect`](/docs/api/qiskit-noise-learning/generated/analysis-flip-post-select "qiskit_noise_learning.analysis.FlipPostSelect")                  | Apply a mask to raw data based on bit flips across measurement outcomes.                                                                                                         |
| [`LegacySolve`](/docs/api/qiskit-noise-learning/generated/analysis-legacy-solve "qiskit_noise_learning.analysis.LegacySolve")                            | Solves for the [`ModelData`](/docs/api/qiskit-noise-learning/generated/data-model-data "qiskit_noise_learning.data.ModelData") using the legacy pair-fidelity method.            |
| [`LinearSystemData`](/docs/api/qiskit-noise-learning/generated/analysis-linear-system-data "qiskit_noise_learning.analysis.LinearSystemData")            | The linear system to solve and metadata in raw format.                                                                                                                           |
| [`LSQLinearSolve`](/docs/api/qiskit-noise-learning/generated/analysis-lsq-linear-solve "qiskit_noise_learning.analysis.LSQLinearSolve")                  | Solves for the [`ModelData`](/docs/api/qiskit-noise-learning/generated/data-model-data "qiskit_noise_learning.data.ModelData") using SciPy's linear least squares solver.        |
| [`NNLSSolve`](/docs/api/qiskit-noise-learning/generated/analysis-nnls-solve "qiskit_noise_learning.analysis.NNLSSolve")                                  | Solves for the [`ModelData`](/docs/api/qiskit-noise-learning/generated/data-model-data "qiskit_noise_learning.data.ModelData") using SciPy's non-negative least squares solver.  |
| [`PositivityMinSolve`](/docs/api/qiskit-noise-learning/generated/analysis-positivity-min-solve "qiskit_noise_learning.analysis.PositivityMinSolve")      | Solves for the [`ModelData`](/docs/api/qiskit-noise-learning/generated/data-model-data "qiskit_noise_learning.data.ModelData") while minimizing Pauli-Lindblad rate positivity.  |
| [`SymmetrizeFidelities`](/docs/api/qiskit-noise-learning/generated/analysis-symmetrize-fidelities "qiskit_noise_learning.analysis.SymmetrizeFidelities") | Project generator rates into the fidelity-symmetry null space, gate by gate.                                                                                                     |
| [`SymmetrizeGenerators`](/docs/api/qiskit-noise-learning/generated/analysis-symmetrize-generators "qiskit_noise_learning.analysis.SymmetrizeGenerators") | Project generator rates to satisfy conjugation symmetry, gate by gate.                                                                                                           |
| [`ZeroPostSelect`](/docs/api/qiskit-noise-learning/generated/analysis-zero-post-select "qiskit_noise_learning.analysis.ZeroPostSelect")                  | Apply a mask to raw data based on whether bit values are all False.                                                                                                              |