qiskit.ignis.characterization.BaseGateFitter
class BaseGateFitter(description, backend_result, xdata, qubits, fit_fun, fit_p0, fit_bounds, circuit_names, series=None, expected_state='0')
Base class for fitters of gate errors
See BaseFitter __init__
__init__
__init__(description, backend_result, xdata, qubits, fit_fun, fit_p0, fit_bounds, circuit_names, series=None, expected_state='0')
See BaseFitter __init__
Methods
__init__(description, backend_result, xdata, …) | See BaseFitter __init__ |
add_data(results[, recalc, refit]) | Add new execution results to previous execution results |
fit_data([qid, p0, bounds, series]) | Fit the curve. |
plot(qind[, series, ax, show_plot]) | Plot err data. |
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
add_data(results, recalc=True, refit=True)
Add new execution results to previous execution results
Parameters
- results (
Union[Result,List[Result]]) – new execution results - recalc (
bool) – whether tp recalculate the data - refit (
bool) – whether to refit the data
backend_result
Return the execution results
Return type
Union[Result, List[Result]]
description
Return the fitter’s purpose, e.g. ‘T1’
Return type
str
fit_data
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 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)
fit_fun
Return the function used in the fit, e.g. BaseFitter._exp_fit_fun
Return type
Callable
measured_qubits
Return the indices of the qubits to be characterized
Return type
List[int]
params
Return the fit function parameters that were calculated by curve_fit
Return type
List[float]
params_err
Return the error of the fit function parameters
Return type
List[float]
plot
plot(qind, series='0', ax=None, show_plot=True)
Plot err data.
Parameters
- qind (int) – qubit index to plot
- series (str) – the series to plot
- ax (Axes) – plot axes
- show_plot (bool) – call plt.show()
Returns
The axes object
Return type
Axes
Raises
ImportError – if matplotlib is not installed
series
Return the list of series for the data
Return type
Optional[List[str]]
xdata
Return the data points on the x-axis, the independenet parameter which is fit against
Return type
Union[List[float], array]
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]