qiskit.ignis.characterization.DragFitter

class DragFitter(backend_result, xdata, qubits, fit_p0, fit_bounds=None)

Drag Experiment fitter

See IQFitter __init__

fit_p0 is [a, x0, c] where the fit is a*(x-x0)^2+c

__init__(backend_result, xdata, qubits, fit_p0, fit_bounds=None)

See IQFitter __init__

fit_p0 is [a, x0, c] where the fit is a*(x-x0)^2+c

Methods

__init__(backend_result, xdata, qubits, fit_p0)	See IQFitter __init__
add_data(results[, recalc, refit])	Add new execution results to previous execution results
drag_amplitude([qind])	Return the drag amplitude from the fit
fit_data([qid, p0, bounds, series])	Fit the curve.
guess_params([qind])	Guess parameters for the drag fit
plot(qind[, series, ax, show_plot])	Plot the data and fit

Attributes

backend_result	Return the execution results
description	Return the fitter’s purpose, e.g.
fit_fun	Return the function used in the fit, e.g.
measured_qubits	Return the indices of the qubits to be characterized
params	Return the fit function parameters that were calculated by curve_fit
params_err	Return the error of the fit function parameters
series	Return the list of series for the data
xdata	Return the data points on the x-axis, the independenet parameter which is fit against
ydata	Return the data points on the y-axis

add_data(results, recalc=True, refit=True)

Add new execution results to previous execution results

Parâmetros

• results (Union[Result, List[Result]]) – new execution results

• recalc (bool) – whether tp recalculate the data

• refit (bool) – whether to refit the data

property backend_result

Return the execution results

Tipo de retorno

Union[Result, List[Result]]

property description

Return the fitter’s purpose, e.g. ‘T1’

Tipo de retorno

str

drag_amplitude(qind=- 1)

Return the drag amplitude from the fit

Parâmetros

qind (int) – qubit index

Retorna

drag amp

Tipo de retorno

float

fit_data(qid=- 1, p0=None, bounds=None, series=None)

Fit the curve.

Compute self._params and self._params_err

Parâmetros

• qid (int) – qubit for fitting. If -1 fit for all the qubits

• p0 (Optional[List[float]]) – initial guess, equivalent to p0 in scipy.optimize

• bounds (Optional[Tuple[List[float], List[float]]]) – bounds, equivalent to bounds in scipy.optimize

• series (Optional[str]) – series to fit (if None fit all)

property fit_fun

Return the function used in the fit, e.g. BaseFitter._exp_fit_fun

Tipo de retorno

Callable

guess_params(qind=0)

Guess parameters for the drag fit

Parâmetros

qind (int) – qubit index

Retorna

guess parameters

[a, x0, c] where the fit is \(a * (x - x0)^{2+c}\)

Tipo de retorno

list

property measured_qubits

Return the indices of the qubits to be characterized

Tipo de retorno

List[int]

property params

Return the fit function parameters that were calculated by curve_fit

Tipo de retorno

List[float]

property params_err

Return the error of the fit function parameters

Tipo de retorno

List[float]

plot(qind, series='0', ax=None, show_plot=False)

Plot the data and fit

Parâmetros

• qind (int) – qubit index

• series (str) – data series to plot (for rabi data always ‘0’)

• ax (Axes) – matploblib axes (if none created)

• show_plot (bool) – do plot.show

Retorna

Plot axes

Tipo de retorno

Axes

property series

Return the list of series for the data

Tipo de retorno

Optional[List[str]]

property xdata

Return the data points on the x-axis, the independenet parameter which is fit against

Tipo de retorno

Union[List[float], array]

property ydata

Return the data points on the y-axis

The data points are returning in the form of a list of dictionaries:

• ydata[i][‘mean’] is a list, where item

  no. j is the probability of success of qubit i for a circuit that lasts xdata[j].

• ydata[i][‘std’] is a list, where ydata[‘std’][j] is the

  standard deviation of the success of qubit i.

Tipo de retorno

List[Dict]