# This code is part of Qiskit.
#
# (C) Copyright IBM 2021.
#
# 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.
"""
T1 Analysis class.
"""
from typing import Union
import numpy as np
import qiskit_experiments.curve_analysis as curve
from qiskit_experiments.framework import Options
[docs]
class T1Analysis(curve.DecayAnalysis):
"""A class to analyze T1 experiments."""
@classmethod
def _default_options(cls) -> Options:
"""Default analysis options."""
options = super()._default_options()
options.plotter.set_figure_options(
xlabel="Delay",
ylabel="P(1)",
xval_unit="s",
)
options.result_parameters = [curve.ParameterRepr("tau", "T1", "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 [0.9, 1.1]
- base is less than 0.1
- 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 - 1.0) < 0.1,
abs(base.nominal_value) < 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"
[docs]
class T1KerneledAnalysis(curve.DecayAnalysis):
"""A class to analyze T1 experiments with kerneled data."""
@classmethod
def _default_options(cls) -> Options:
"""Default analysis options."""
options = super()._default_options()
options.plotter.set_figure_options(
xlabel="Delay",
ylabel="Normalized Projection on the Main Axis",
xval_unit="s",
)
options.result_parameters = [curve.ParameterRepr("tau", "T1", "s")]
options.normalization = True
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 [0.9, 1.1]
- base is less than 0.1
- 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 - 1.0) < 0.1,
abs(base.nominal_value) < 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"
def _format_data(
self,
curve_data: curve.ScatterTable,
category: str = "formatted",
) -> curve.ScatterTable:
"""Postprocessing for preparing the fitting data.
Args:
curve_data: Processed dataset created from experiment results.
category: Category string of the output dataset.
Returns:
New scatter table instance including fit data.
"""
# check if the SVD decomposition categorized 0 as 1 by calculating the average slope
diff_y = np.diff(curve_data.y)
avg_slope = sum(diff_y) / len(diff_y)
if avg_slope > 0:
curve_data.y = 1 - curve_data.y
return super()._format_data(curve_data)