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.

DragCalAnalysis¶

class DragCalAnalysis(models=None, name=None)[source]¶

Drag calibration analysis 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 a Drag calibration experiment by fitting multiple series each to a cosine function. All functions share the phase parameter (i.e. beta), amplitude, and baseline. The frequencies of the oscillations are related through the number of repetitions of the Drag gates. Several initial guesses are tried if the user does not provide one. The fit function is

\[y_i = {\rm amp} \cos\left(2 \pi\cdot {\rm reps}_i \cdot {\rm freq}\cdot x - 2 \pi\cdot {\rm reps}_i \cdot {\rm freq}\cdot \beta\right) + {\rm base}\]

Here, the fit parameter \(freq\) is the frequency of the oscillation of a single pair of Drag plus and minus rotations and \({\rm reps}_i\) is the number of times that the Drag plus and minus rotations are repeated in curve \(i\). Note that the aim of the Drag calibration is to find the \(\beta\) that minimizes the phase shifts. This implies that the optimal \(\beta\) occurs when all \(y\) curves are minimum, i.e. they produce the ground state. This occurs when

\[{\rm reps}_i * {\rm freq} * (x - \beta) = N\]

is satisfied with \(N\) an integer. Note, however, that this condition produces a minimum only when the amplitude is negative. To ensure this is the case, we bound the amplitude to be less than 0.

Fit parameters

The following fit parameters are estimated during the analysis.

Descriptions

\(\rm amp\): Amplitude of all series.
\(\rm base\): Base line of all series.
\({\rm freq}\): Frequency of oscillation as a function of \(\beta\) for a single pair of DRAG plus and minus pulses.
\(\beta\): Common beta offset. This is the parameter of interest.

Initial Guess

\(\rm amp\): The maximum y value scaled by -1, -0.5, and -0.25.
\(\rm base\): Half the maximum y-value of the data.
\({\rm freq}\): For the curve with the most Drag pulse repetitions, the peak frequency of the power spectral density is found and then divided by the number of repetitions.
\(\beta\): Linearly spaced between the maximum and minimum scanned beta.

Boundaries

\(\rm amp\): [-2, 0] scaled to the maximum signal value.
\(\rm base\): [-1, 1] scaled to the maximum y-value.
\({\rm freq}\): [0, inf].
\(\beta\): [-min scan range, max scan range].

Analysis options

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

Options

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: True
  
  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: ["beta"]
  
  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.