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.

ErrorAmplificationAnalysis¶

class ErrorAmplificationAnalysis(name=None)[source]¶

Error amplification analysis class based on a fit to a cosine function.

Fit model

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

Analyse an error amplifying calibration experiment by fitting the data to a cosine function. The user must also specify the intended rotation angle per gate, here labeled, \({\rm apg}\). The parameter of interest in the fit is the deviation from the intended rotation angle per gate labeled \({\rm d}\theta\). The fit function is

\[y = \frac{{\rm amp}}{2}\cos\left(x[{\rm d}\theta + {\rm apg} ] \ -{\rm phase\_offset}\right)+{\rm base}\]

To understand how the error is measured we can transformed the function above into

\[y = \frac{{\rm amp}}{2} \left(\ \cos\right({\rm d}\theta \cdot x\left)\ \cos\right({\rm apg} \cdot x - {\rm phase\_offset}\left) -\ \sin\right({\rm d}\theta \cdot x\left)\ \sin\right({\rm apg} \cdot x - {\rm phase\_offset}\left) \right) + {\rm base}\]

When \({\rm apg} \cdot x - {\rm phase\_offset} = (2n + 1) \pi/2\) is satisfied the fit model above simplifies to

\[y = \mp \frac{{\rm amp}}{2} \sin\left({\rm d}\theta \cdot x\right) + {\rm base}\]

In the limit \({\rm d}\theta \ll 1\), the error can be estimated from the curve data

\[{\rm d}\theta \simeq \mp \frac{2(y - {\rm base})}{x \cdot {\rm amp}}\]

Fit parameters

The following fit parameters are estimated during the analysis.

Descriptions

\(\rm amp\): Amplitude of the oscillation.
\(\rm base\): Base line.
\(d\theta\): The angle offset in the gate that we wish to measure.

Initial Guess

\(\rm amp\): The maximum y value less the minimum y value.
\(\rm base\): The average of the data.
\(d\theta\): Multiple initial guesses are tried ranging from -a to a where a is given by max(abs(angle_per_gate), np.pi / 2). Extra guesses are added based on curve data when either \(\rm amp\) or \(\rm base\) is \(\pi/2\). See fit model for details.

Boundaries

\(\rm amp\): [-2, 2] scaled to the maximum signal value.
\(\rm base\): [-1, 1] scaled to the maximum signal value.
\(d\theta\): [-0.8 pi, 0.8 pi]. The bounds do not include plus and minus pi since these values often correspond to symmetry points of the fit function. Furthermore, this type of analysis is intended for values of \(d\theta\) close to zero.

Analysis options

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

Options

Defined in the class ErrorAmplificationAnalysis:
- max_good_angle_error (float)
  
  Default value: 1.5707963267948966
  
  The maximum angle error for which the fit is considered as good. Defaults to \(\pi/2\).
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: False
  
  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: "shots_weighted"
  
  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: ["d_theta"]
  
  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.