Source code for qiskit_experiments.library.characterization.analysis.t2hahn_analysis

# This code is part of Qiskit.
#
# (C) Copyright IBM 2022.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
"""
T2 Hahn echo Analysis class.
"""
from typing import Union

import numpy as np

import qiskit_experiments.curve_analysis as curve
from qiskit_experiments.data_processing import DataProcessor, Probability

from qiskit_experiments.framework import Options


[docs] class T2HahnAnalysis(curve.DecayAnalysis): """A class to analyze T2Hahn experiments.""" @classmethod def _default_options(cls) -> Options: """Default analysis options.""" options = super()._default_options() options.plotter.set_figure_options( xlabel="Delay", ylabel="P(0)", xval_unit="s", ) options.data_processor = DataProcessor( input_key="counts", data_actions=[Probability(outcome="0")] ) options.bounds = { "amp": (0.0, 1.0), "tau": (0.0, np.inf), "base": (0.0, 1.0), } options.result_parameters = [curve.ParameterRepr("tau", "T2", "s")] return options def _evaluate_quality(self, fit_data: curve.CurveFitResult) -> Union[str, None]: """Algorithmic criteria for whether the fit is good or bad. A good fit has: - a reduced chi-squared lower than three and greater than zero - absolute amp is within [0.4, 0.6] - base is less is within [0.4, 0.6] - amp error is less than 0.1 - tau error is less than its value - base error is less than 0.1 """ amp = fit_data.ufloat_params["amp"] tau = fit_data.ufloat_params["tau"] base = fit_data.ufloat_params["base"] criteria = [ 0 < fit_data.reduced_chisq < 3, abs(amp.nominal_value - 0.5) < 0.1, abs(base.nominal_value - 0.5) < 0.1, curve.utils.is_error_not_significant(amp, absolute=0.1), curve.utils.is_error_not_significant(tau), curve.utils.is_error_not_significant(base, absolute=0.1), ] if all(criteria): return "good" return "bad"