qiskit.ignis.characterization.RabiFitter¶
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class
RabiFitter(backend_result, xdata, qubits, fit_p0, fit_bounds=None)[source]¶ Rabi Experiment fitter
See BaseCalibrationFitter __init__
fit_po is [amp, freq, phase, offset]
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__init__(backend_result, xdata, qubits, fit_p0, fit_bounds=None)[source]¶ See BaseCalibrationFitter __init__
fit_po is [amp, freq, phase, offset]
Methods
__init__(backend_result, xdata, qubits, fit_p0)See BaseCalibrationFitter __init__
add_data(results[, recalc, refit])Add new execution results to previous execution results
fit_data([qid, p0, bounds, series])Fit the curve.
guess_params([qind])Guess fit parameters for rabi oscillation data
pi2_amplitude([qind])Return the pi/2 amplitude from the fit
pi_amplitude([qind])Return the pi amplitude from the fit
plot(qind[, series, ax, show_plot])Plot the data and fit
Attributes
Return the execution results
Return the fitter’s purpose, e.g.
Return the function used in the fit, e.g.
Return the indices of the qubits to be characterized
Return the fit function parameters that were calculated by curve_fit
Return the error of the fit function parameters
Return the list of series for the data
Return the data points on the x-axis, the independenet parameter which is fit against
Return the data points on the y-axis
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add_data(results, recalc=True, refit=True)¶ Add new execution results to previous execution results
- Parameters
results (
Union[Result,List[Result]]) – new execution resultsrecalc (
bool) – whether tp recalculate the datarefit (
bool) – whether to refit the data
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property
backend_result¶ Return the execution results
- Return type
Union[Result,List[Result]]
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property
description¶ Return the fitter’s purpose, e.g. ‘T1’
- Return type
str
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fit_data(qid=- 1, p0=None, bounds=None, series=None)¶ Fit the curve.
Compute self._params and self._params_err
- Parameters
qid (
int) – qubit for fitting. If -1 fit for all the qubitsp0 (
Optional[List[float]]) – initial guess, equivalent to p0 in scipy.optimizebounds (
Optional[Tuple[List[float],List[float]]]) – bounds, equivalent to bounds in scipy.optimizeseries (
Optional[str]) – series to fit (if None fit all)
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property
fit_fun¶ Return the function used in the fit, e.g. BaseFitter._exp_fit_fun
- Return type
Callable
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guess_params(qind=0)[source]¶ Guess fit parameters for rabi oscillation data
- Parameters
qind (int) – qubit index to guess fit parameters for
- Returns
- List of fit guess parameters
[amp, freq, phase, offset]
- Return type
list
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property
measured_qubits¶ Return the indices of the qubits to be characterized
- Return type
List[int]
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property
params¶ Return the fit function parameters that were calculated by curve_fit
- Return type
List[float]
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property
params_err¶ Return the error of the fit function parameters
- Return type
List[float]
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pi2_amplitude(qind=- 1)[source]¶ Return the pi/2 amplitude from the fit
- Parameters
qind (int) – qubit index
- Returns
\(\frac{\pi}{2}\) amp
- Return type
float
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pi_amplitude(qind=- 1)[source]¶ Return the pi amplitude from the fit
- Parameters
qind (int) – qubit index
- Returns
\(\pi\) amp
- Return type
float
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plot(qind, series='0', ax=None, show_plot=False)[source]¶ Plot the data and fit
- Parameters
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
- Returns
Plot axes
- Return type
Axes
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property
series¶ Return the list of series for the data
- Return type
Optional[List[str]]
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property
xdata¶ Return the data points on the x-axis, the independenet parameter which is fit against
- Return type
Union[List[float],array]
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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.
- Return type
List[Dict]
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