CrossResonanceHamiltonianAnalysis¶
- class CrossResonanceHamiltonianAnalysis[source]¶
A class to analyze cross resonance Hamiltonian tomography experiment.
Fit model
This is the curve fitting analysis. The following equation(s) are used to represent curve(s).
This analysis performs
BlochTrajectoryAnalysis
on the target qubit with the control qubit states in \(\in \{ |0\rangle, |1\rangle \}\).Based on the fit result, cross resonance Hamiltonian coefficients can be determined by
\[\begin{split}ZX &= \frac{p_{x, |0\rangle} - p_{x, |1\rangle}}{2}, \\ ZY &= \frac{p_{y, |0\rangle} - p_{y, |1\rangle}}{2}, \\ ZZ &= \frac{p_{z, |0\rangle} - p_{z, |1\rangle}}{2}, \\ IX &= \frac{p_{x, |0\rangle} + p_{x, |1\rangle}}{2}, \\ IY &= \frac{p_{y, |0\rangle} + p_{y, |1\rangle}}{2}, \\ IZ &= \frac{p_{z, |0\rangle} + p_{z, |1\rangle}}{2},\end{split}\]where \(p_{\beta, |j\rangle}\) is a fit parameter of
BlochTrajectoryAnalysis
for the projection axis \(\beta\) with the control qubit state \(|j\rangle\).Analysis options
These are the keyword arguments of the
run()
method.- Options
Defined in the class
CompositeCurveAnalysis
:plotter (BasePlotter)
Default value: Instance ofCurvePlotter
A plotter instance to visualize the analysis result.plot (bool)
Default value:True
SetTrue
to create figure for fit result. This isTrue
by default.return_fit_parameters (bool)
Default value:False
(Deprecated) SetTrue
to return all fit model parameters with details of the fit outcome. Default toFalse
.extra (Dict[str, Any])
Default value: {}A dictionary that is appended to all database entries as extra information.
Defined in the class
BaseAnalysis
:figure_names (str or List[str])
Default value:None
Identifier of figures that appear in the experiment data to sort figures by name.
See also
Initialization
Deprecated since version 0.8: The class
qiskit_experiments.library.characterization.analysis.cr_hamiltonian_analysis.CrossResonanceHamiltonianAnalysis
is deprecated as of qiskit-experiments 0.8. It will be removed no earlier than 3 months after the release date. Due to the deprecation of Qiskit Pulse, experiments and related classses involving pulse gate calibrations like this one have been deprecated.Attributes
- models¶
Return fit models.
- name¶
Return name of this analysis.
- parameters¶
Return parameters of this curve analysis.
- plotter¶
A short-cut to the plotter instance.
Methods
- analyses(index=None)¶
Return curve analysis instance.
- Parameters:
index (int | str | None) – Name of group or numerical index.
- Returns:
Curve analysis instance.
- Return type:
BaseCurveAnalysis | List[BaseCurveAnalysis]
- config()¶
Return the config dataclass for this analysis
- Return type:
- copy()¶
Return a copy of the analysis
- Return type:
- classmethod from_config(config)¶
Initialize an analysis class from analysis config
- Return type:
- run(experiment_data, replace_results=False, **options)¶
Run analysis and update ExperimentData with analysis result.
- Parameters:
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.
- Returns:
An experiment data object containing analysis results, figures, and artifacts.
- Raises:
QiskitError – If experiment_data container is not valid for analysis.
- Return type:
Note
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.