Analysis

qiskit_noise_learning.analysis

Data analysis.

Classes

Column 1	Column 2
`AnalysisPipeline`	A composite `AnalysisStage` that chains stages sequentially.
`AnalysisStage`	Abstract base for a stage in the analysis pipeline.
`AverageObservables`	Average observables over randomizations for each unbound path and fragment depth pair.
`ComputeObservables`	Compute observable data from raw data.
`CurveFitObservables`	Fit observable data to exponential decays of the form `a * f**fragment_depth`, and average any remaining observables over randomizations.
`Fit`	Container for data at each level of the analysis hierarchy.
`FlipPostSelect`	Apply a mask to raw data based on bit flips across measurement outcomes.
`LegacySolve`	Solves for the `ModelData` using the legacy pair-fidelity method.
`LinearSystemData`	The linear system to solve and metadata in raw format.
`LSQLinearSolve`	Solves for the `ModelData` using SciPy's linear least squares solver.
`NNLSSolve`	Solves for the `ModelData` using SciPy's non-negative least squares solver.
`PositivityMinSolve`	Solves for the `ModelData` while minimizing Pauli-Lindblad rate positivity.
`SymmetrizeFidelities`	Project generator rates into the fidelity-symmetry null space, gate by gate.
`SymmetrizeGenerators`	Project generator rates to satisfy conjugation symmetry, gate by gate.
`ZeroPostSelect`	Apply a mask to raw data based on whether bit values are all False.

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