Note

This is the documentation for the current state of the development branch of Qiskit Experiments. The documentation or APIs here can change prior to being released.

CompositeCurveAnalysis

class CompositeCurveAnalysis(analyses, name=None)

Composite Curve Analysis.

The CompositeCurveAnalysis takes multiple curve analysis instances and performs each analysis on the same experimental results. These analyses are performed independently, thus fit parameters have no correlation. Note that this is different from CompositeAnalysis which analyses the outcome of a composite experiment, in which multiple different experiments are performed. The CompositeCurveAnalysis is attached to a single experiment instance, which may execute similar circuits with slightly different settings. Experiments with different settings might be distinguished by the circuit metadata. The outcomes of the same set of experiments are assigned to a specific analysis instance in the composite curve analysis. This mapping is usually done with the analysis option filter_data dictionary. Otherwise, all analyses are performed on the same set of outcomes.

Examples

In this example, we write up a composite analysis consisting of two oscillation analysis instances, assuming two Rabi experiments in 1-2 subspace starting with different initial states $\in \{|0⟩,|1⟩\}$. This is a typical procedure to measure the thermal population of the qubit.

from qiskit_experiments import curve_analysis as curve

analyses = []
for qi in (0, 1):
    analysis = curve.OscillationAnalysis(name=f"init{qi}")
    analysis.set_options(
        return_fit_parameters=["freq"],
        filter_data={"init_state": qi},
    )
analysis = CompositeCurveAnalysis(analyses=analyses)

This analysis will return two analysis result data for the fit parameter “freq” for experiments with the initial state $|0⟩$ and $|1⟩$. The experimental circuits starting with different initial states must be distinguished by the circuit metadata {"init_state": 0} or {"init_state": 1}, along with the “xval” in the same dictionary. If you want to compute another quantity using two fitting outcomes, you can override CompositeCurveAnalysis._create_curve_data() in subclass.

CompositeCurveAnalysis subclass may override following methods.

_evaluate_quality

This method evaluates the quality of the composite fit based on the all analysis outcomes. This returns “good” when all fit outcomes are evaluated as “good”, otherwise it returns “bad”.

_create_analysis_results

This method is passed all the group fit outcomes and can return a list of new values to be stored in the analysis results.

Initialize the analysis object.

Attributes

CompositeCurveAnalysis.drawer	A short-cut for curve drawer instance, if set.
CompositeCurveAnalysis.models	Return fit models.
CompositeCurveAnalysis.name	Return name of this analysis.
CompositeCurveAnalysis.options	Return the analysis options for run() method.
CompositeCurveAnalysis.parameters	Return parameters of this curve analysis.
CompositeCurveAnalysis.plotter	A short-cut to the plotter instance.

Methods

CompositeCurveAnalysis.analyses([index])	Return curve analysis instance.
CompositeCurveAnalysis.config()	Return the config dataclass for this analysis
CompositeCurveAnalysis.copy()	Return a copy of the analysis
CompositeCurveAnalysis.from_config(config)	Initialize an analysis class from analysis config
CompositeCurveAnalysis.run(experiment_data)	Run analysis and update ExperimentData with analysis result.
CompositeCurveAnalysis.set_options(**fields)	Set the analysis options for run() method.