RBAnalysis¶

class RBAnalysis[source]¶

A class to analyze randomized benchmarking experiments.

Overview

This analysis takes only single series. This series is fit by the exponential decay function. From the fit \(\alpha\) value this analysis estimates the error per Clifford (EPC).

When analysis option gate_error_ratio is provided, this analysis also estimates errors of individual gates assembling a Clifford gate. In computation of two-qubit EPC, this analysis can also decompose the contribution from the underlying single qubit depolarizing channels when epg_1_qubit analysis option is provided [1].

Fit model

This is the curve fitting analysis. The following equation(s) are used to represent curve(s).

\[F(x) = a \alpha^x + b\]

Fit parameters

The following fit parameters are estimated during the analysis.

Descriptions

\(a\): Height of decay curve.
\(b\): Base line.
\(\alpha\): Depolarizing parameter.

Initial Guess

\(a\): Determined by \(1 - b\).
\(b\): Determined by \((1/2)^n\) where \(n\) is number of qubit.
\(\alpha\): Determined by rb_decay().

Boundaries

\(a\): [0, 1]
\(b\): [0, 1]
\(\alpha\): [0, 1]

References

[1] David C. McKay, Sarah Sheldon, John A. Smolin, Jerry M. Chow, Jay M. Gambetta, Three Qubit Randomized Benchmarking, Phys. Rev. Lett. 122, 200502 (2019), doi: 10.1103/PhysRevLett.122.200502 (open)

Analysis options

These are the keyword arguments of the run() method.

Options

Defined in the class RBAnalysis:
- gate_error_ratio (Optional[Dict[str, float]])
  
  Default value: "default"
  
  A dictionary with gate name keys and error ratio values used when calculating EPG from the estimated EPC. The default value will use standard gate error ratios. If you don’t know accurate error ratio between your basis gates, you can skip analysis of EPGs by setting this options to None.
- epg_1_qubit (List[AnalysisResult])
  
  Default value: None
  
  Analysis results from previous RB experiments for individual single qubit gates. If this is provided, EPC of 2Q RB is corrected to exclude the depolarization of underlying 1Q channels.
Defined in the class BaseCurveAnalysis:
- plotter (BasePlotter)
  
  Default value: Instance of CurvePlotter
  
  A curve plotter instance to visualize the analysis result.
- plot_raw_data (bool)
  
  Default value: True
  
  Set True to draw processed data points, dataset without formatting, on canvas. This is False by default.
- plot_residuals (bool)
  
  Default value: False
  
  Set True to draw the residuals data for the fitting model. This is False by default.
- return_fit_parameters (bool)
  
  Default value: True
  
  (Deprecated) Set True to return all fit model parameters with details of the fit outcome. Default to False.
- data_processor (Callable)
  
  Default value: None
  
  A callback function to format experiment data. This can be a DataProcessor instance that defines the self.__call__ method.
- normalization (bool)
  
  Default value: False
  
  Set True to normalize y values within range [-1, 1]. Default to False.
- average_method (Literal[“sample”, “iwv”, “shots_weighted”])
  
  Default value: "sample"
  
  Method to average the y values when the same x values appear multiple times. One of “sample”, “iwv” (i.e. inverse weighted variance), “shots_weighted”. See mean_xy_data() for details. Default to “shots_weighted”.
- p0 (Dict[str, float])
  
  Default value: {}
  
  Initial guesses for the fit parameters. The dictionary is keyed on the fit parameter names.
- bounds (Dict[str, Tuple[float, float]])
  
  Default value: {}
  
  Boundary of fit parameters. The dictionary is keyed on the fit parameter names and values are the tuples of (min, max) of each parameter.
- fit_method (str)
  
  Default value: "least_squares"
  
  Fit method that LMFIT minimizer uses. Default to least_squares method which implements the Trust Region Reflective algorithm to solve the minimization problem. See LMFIT documentation for available options.
- lmfit_options (Dict[str, Any])
  
  Default value: {}
  
  Options that are passed to the LMFIT minimizer. Acceptable options depend on fit_method.
- x_key (str)
  
  Default value: "xval"
  
  Circuit metadata key representing a scanned value.
- fit_category (str)
  
  Default value: "formatted"
  
  Name of dataset in the scatter table to fit.
- result_parameters (List[Union[str, ParameterRepr])
  
  Default value: ["alpha"]
  
  Parameters reported in the database as a dedicated entry. This is a list of parameter representation which is either string or ParameterRepr object. If you provide more information other than name, you can specify [ParameterRepr("alpha", "α", "a.u.")] for example. The parameter name should be defined in the series definition. Representation should be printable in standard output, i.e. no latex syntax.
- extra (Dict[str, Any])
  
  Default value: {}
  
  A dictionary that is appended to all database entries as extra information.
- fixed_parameters (Dict[str, Any])
  
  Default value: {}
  
  Fitting model parameters that are fixed during the curve fitting. This should be provided with default value keyed on one of the parameter names in the series definition.
- filter_data (Dict[str, Any])
  
  Default value: {}
  
  Dictionary of experiment data metadata to filter. Experiment outcomes with metadata that matches with this dictionary are used in the analysis. If not specified, all experiment data are input to the curve fitter. By default, no filtering condition is set.
- data_subfit_map (Dict[str, Dict[str, Any]])
  
  Default value: {}
  
  The mapping of experiment result data to sub-fit models. This dictionary is keyed on the LMFIT model name, and the value is a sorting key-value pair that filters the experiment results, and the filtering is done based on the circuit metadata.
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.