qiskit_noise_learning.protocols.process_learning_result

qiskit_noise_learning.protocols.process_learning_result(result: QuantumProgramResult, raw_data_stage: AnalysisStage | None = None) → Fit

Analyze the result of a program built by prepare_learning_program().

Applies the standard “vanilla” learning program analysis using curve fitting and LegacySolve fit the model.

Example:

fit = process_learning_result(job.result())
noise_maps = split_pauli_lindblad_model(fit.model).model.to_pauli_lindblad_maps(
    fit.model_data
)

Parameters

• result – The result of a program built by prepare_learning_program().
• raw_data_stage – An optional analysis stage to run on the raw data ahead of the standard analysis, typically to post-select on registers that a pass manager added during preparation. It must consume and produce RawData.

Returns

The fit, holding the data at every analysis level.

Raises

• TypeError – If raw_data_stage is not an analysis stage.
• ValueError – If raw_data_stage does not consume and produce RawData, if result holds no program items, if the result’s passthrough data was written in an incompatible format version, or if the result violates the solver’s assumptions.