class CurveAnalysis(models=None, name=None)[source]#

Base class for curve analysis with single curve group.

The fit parameters from the series defined under the analysis class are all shared and the analysis performs a single multi-objective function optimization.

A subclass may override these methods to customize the fit workflow.


This method performs data processing and returns the processed dataset. By default, it internally calls the DataProcessor instance from the data_processor analysis option and processes the experiment data payload to create Y data with uncertainty. X data and other metadata are generated within this method by inspecting the circuit metadata. The series classification is also performed based upon the matching of circuit metadata and SeriesDef.filter_kwargs.


This method consumes the processed dataset and outputs the formatted dataset. By default, this method takes the average of y values over the same x values and then sort the entire data by x values.


This method creates initial guesses for the fit parameters. See Providing initial guesses for details.


This method performs the fitting with predefined fit models and the formatted dataset. This method internally calls the _generate_fit_guesses() method. Note that this is a core functionality of the _run_analysis() method, that creates fit result objects from the formatted dataset.


This method evaluates the quality of the fit based on the fit result. This returns “good” when reduced chi-squared is less than 3.0. Usually it returns string “good” or “bad” according to the evaluation.


This method creates analysis results for important fit parameters that might be defined by analysis options result_parameters.


This method creates figures by consuming the scatter table data. Figures are created when the analysis option plot is True.


This method initializes analysis options against input experiment data. Usually this method is called before other methods are called.

Initialize data fields that are privately accessed by methods.

  • models (List[Model] | None) – List of LMFIT Model class to define fitting functions and parameters. If multiple models are provided, the analysis performs multi-objective optimization where the parameters with the same name are shared among provided models. When multiple models are provided, user must specify the data_subfit_map value in the analysis options to allocate experimental results to a particular fit model.

  • name (str | None) – Optional. Name of this analysis.



A short-cut for curve drawer instance, if set. None otherwise.

Deprecated since version 0.5: The method qiskit_experiments.curve_analysis.base_curve_analysis.BaseCurveAnalysis.drawer() is deprecated as of qiskit-experiments 0.5. It will be removed after 0.6. Use plotter from the new visualization module.


Return fit models.


Return name of this analysis.


Return the analysis options for run() method.


Return parameters of this curve analysis.


A short-cut to the curve plotter instance.



Return the config dataclass for this analysis

Return type:



Return a copy of the analysis

Return type:


classmethod from_config(config)#

Initialize an analysis class from analysis config

Return type:



Return model names.

Return type:


run(experiment_data, replace_results=False, **options)#

Run analysis and update ExperimentData with analysis result.

  • experiment_data (ExperimentData) – the experiment data to analyze.

  • replace_results (bool) – If True clear any existing analysis results, figures, and artifacts in the experiment data and replace with new results. See note for additional information.

  • options – additional analysis options. See class documentation for supported options.


An experiment data object containing analysis results, figures, and artifacts.


QiskitError – If experiment_data container is not valid for analysis.

Return type:



Updating Results

If analysis is run with replace_results=True then any analysis results, figures, and artifacts in the experiment data will be cleared and replaced with the new analysis results. Saving this experiment data will replace any previously saved data in a database service using the same experiment ID.

If analysis is run with replace_results=False and the experiment data being analyzed has already been saved to a database service, or already contains analysis results or figures, a copy with a unique experiment ID will be returned containing only the new analysis results and figures. This data can then be saved as its own experiment to a database service.


Set the analysis options for run() method.


fields – The fields to update the options