# Repository: https://gitlab.com/quantify-os/quantify-core
# Licensed according to the LICENCE file on the main branch
"""Module containing analyses for common single qubit timedomain experiments."""
from __future__ import annotations
from typing import Literal, Union
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
from quantify_core.analysis import base_analysis as ba
from quantify_core.analysis import fitting_models as fm
from quantify_core.analysis.calibration import (
has_calibration_points,
rotate_to_calibrated_axis,
)
from quantify_core.data.types import TUID
from quantify_core.visualization.mpl_plotting import (
plot_fit,
plot_textbox,
set_suptitle_from_dataset,
set_xlabel,
set_ylabel,
)
from quantify_core.visualization.SI_utilities import format_value_string
[docs]
class SingleQubitTimedomainAnalysis(ba.BaseAnalysis):
"""
Base Analysis class for single-qubit timedomain experiments.
"""
# pylint: disable=attribute-defined-outside-init, arguments-differ, line-too-long
[docs]
def run(self, calibration_points: Union[bool, Literal["auto"]] = "auto"):
r"""
Parameters
----------
calibration_points
Indicates if the data analyzed includes calibration points. If set to
:code:`True`, will interpret the last two data points in the dataset as
:math:`|0\rangle` and :math:`|1\rangle` respectively. If ``"auto"``, will
use :func:`~.has_calibration_points` to determine if the data contains
calibration points.
Returns
-------
:class:`~.SingleQubitTimedomainAnalysis`:
The instance of this analysis.
""" # NB the return type need to be specified manually to avoid circular import
if not (calibration_points == "auto" or isinstance(calibration_points, bool)):
raise ValueError(
f"Incorrect input. calibration_points={calibration_points} "
"must be on of False, True or 'auto'."
)
self.calibration_points = calibration_points
return super().run()
[docs]
def process_data(self):
"""
Processes the data so that the analysis can make assumptions on the format.
Populates self.dataset_processed.S21 with the complex (I,Q) valued transmission,
and if calibration points are present for the 0 and 1 state, populates
self.dataset_processed.pop_exc with the excited state population.
"""
if not hasattr(self.dataset, "y1"):
self.dataset_processed["S21"] = self.dataset.y0
elif self.dataset.y1.units == "deg":
self.dataset_processed["S21"] = self.dataset.y0 * np.exp(
1j * np.deg2rad(self.dataset.y1)
)
else:
self.dataset_processed["S21"] = self.dataset.y0 + 1j * self.dataset.y1
self.dataset_processed.S21.attrs["name"] = "S21"
self.dataset_processed.S21.attrs["units"] = self.dataset.y0.units
self.dataset_processed.S21.attrs["long_name"] = "Transmission $S_{21}$"
if self.calibration_points == "auto":
self.calibration_points = has_calibration_points(
self.dataset_processed.S21.values
)
if self.calibration_points:
self._rotate_to_calibrated_axis()
def _rotate_to_calibrated_axis(self, ref_idx_0: int = -2, ref_idx_1: int = -1):
ref_val_0 = self.dataset_processed.S21[ref_idx_0]
ref_val_1 = self.dataset_processed.S21[ref_idx_1]
pop_exc = np.real(
rotate_to_calibrated_axis(
data=self.dataset_processed.S21,
ref_val_0=ref_val_0,
ref_val_1=ref_val_1,
)
)
self.dataset_processed["pop_exc"] = pop_exc
self.dataset_processed.pop_exc.attrs["name"] = "pop_exc"
self.dataset_processed.pop_exc.attrs["units"] = ""
self.dataset_processed.pop_exc.attrs["long_name"] = r"$|1\rangle$ population"
self.dataset_processed = self.dataset_processed.swap_dims({"dim_0": "x0"})
def _choose_data_for_fit(self):
if self.calibration_points:
# the last two data points are omitted from the fit as these are cal points
y_data = self.dataset_processed.pop_exc.values[:-2]
x_data = self.dataset_processed.x0.values[:-2]
else:
# if no calibration points are present, fit the magnitude of the signal
y_data = np.abs(self.dataset_processed.S21.values)
x_data = self.dataset_processed.x0.values
return x_data, y_data
# pylint: disable=too-few-public-methods
[docs]
class _DecayFigMixin:
"""A mixin for common analysis logic."""
[docs]
class T1Analysis(SingleQubitTimedomainAnalysis, _DecayFigMixin):
"""
Analysis class for a qubit T1 experiment,
which fits an exponential decay and extracts the T1 time.
"""
[docs]
def run_fitting(self):
"""
Fit the data to :class:`~quantify_core.analysis.fitting_models.ExpDecayModel`.
"""
model = fm.ExpDecayModel()
delay, data = self._choose_data_for_fit()
guess_pars = model.guess(data, delay=delay)
if self.calibration_points:
# if the data is on corrected axes certain parameters can be fixed
model.set_param_hint("offset", value=0, vary=False)
model.set_param_hint("amplitude", value=1, vary=False)
# this call provides updated guess_pars, model.guess is still needed.
guess_pars = model.make_params()
fit_result = model.fit(data, params=guess_pars, t=delay)
self.fit_results.update({"exp_decay_func": fit_result})
[docs]
def analyze_fit_results(self):
"""
Checks fit success and populates :code:`.quantities_of_interest`.
"""
fit_result = self.fit_results["exp_decay_func"]
fit_warning = ba.wrap_text(ba.check_lmfit(fit_result))
# If there is a problem with the fit, display an error message in the text box.
# Otherwise, display the parameters as normal.
if fit_warning is None:
self.quantities_of_interest["fit_success"] = True
text_msg = "Summary\n"
text_msg += format_value_string(r"$T1$", fit_result.params["tau"], unit="s")
if not self.calibration_points:
unit = self.dataset_processed.S21.units
text_msg += format_value_string(
"\namplitude",
fit_result.params["amplitude"],
end_char="\n",
unit=unit,
)
text_msg += format_value_string(
"offset", fit_result.params["offset"], unit=unit
)
else:
text_msg = ba.wrap_text(fit_warning)
self.quantities_of_interest["fit_success"] = False
self.quantities_of_interest["T1"] = ba.lmfit_par_to_ufloat(
fit_result.params["tau"]
)
self.quantities_of_interest["fit_msg"] = text_msg
[docs]
class EchoAnalysis(SingleQubitTimedomainAnalysis, _DecayFigMixin):
"""
Analysis class for a qubit spin-echo experiment,
which fits an exponential decay and extracts the T2_echo time.
"""
[docs]
def run_fitting(self):
"""
Fit the data to :class:`~quantify_core.analysis.fitting_models.ExpDecayModel`.
"""
model = fm.ExpDecayModel()
delay, data = self._choose_data_for_fit()
guess_pars = model.guess(data, delay=delay)
if self.calibration_points:
# if the data is on corrected axes certain parameters can be fixed
model.set_param_hint("offset", value=0.5, vary=False)
model.set_param_hint("amplitude", value=-0.5, vary=False)
# this call provides updated guess_pars, model.guess is still needed.
guess_pars = model.make_params()
fit_result = model.fit(data, params=guess_pars, t=delay)
self.fit_results.update({"exp_decay_func": fit_result})
[docs]
def analyze_fit_results(self):
"""
Checks fit success and populates :code:`.quantities_of_interest`.
"""
fit_result = self.fit_results["exp_decay_func"]
fit_warning = ba.wrap_text(ba.check_lmfit(fit_result))
# If there is a problem with the fit, display an error message in the text box.
# Otherwise, display the parameters as normal.
if fit_warning is None:
self.quantities_of_interest["fit_success"] = True
text_msg = "Summary\n"
text_msg += format_value_string(
r"$T_{2,\mathrm{Echo}}$", fit_result.params["tau"], unit="s"
)
if not self.calibration_points:
unit = self.dataset_processed.S21.units
text_msg += format_value_string(
"\namplitude",
fit_result.params["amplitude"],
end_char="\n",
unit=unit,
)
text_msg += format_value_string(
"offset", fit_result.params["offset"], unit=unit
)
else:
text_msg = ba.wrap_text(fit_warning)
self.quantities_of_interest["fit_success"] = False
self.quantities_of_interest["t2_echo"] = ba.lmfit_par_to_ufloat(
fit_result.params["tau"]
)
self.quantities_of_interest["fit_msg"] = text_msg
[docs]
class RamseyAnalysis(SingleQubitTimedomainAnalysis, _DecayFigMixin):
"""
Fits a decaying cosine curve to Ramsey data (possibly with artificial detuning)
and finds the true detuning, qubit frequency and T2* time.
"""
# Override the run method so that we can add the new optional arguments
# pylint: disable=attribute-defined-outside-init, arguments-differ
[docs]
def run(
self,
artificial_detuning: float = 0,
qubit_frequency: float = None,
calibration_points: Union[bool, Literal["auto"]] = "auto",
):
r"""
Parameters
----------
artificial_detuning
The detuning in Hz that will be emulated by adding an extra phase in
software.
qubit_frequency
The initial recorded value of the qubit frequency (before
accurate fitting is done) in Hz.
calibration_points
Indicates if the data analyzed includes calibration points. If set to
:code:`True`, will interpret the last two data points in the dataset as
:math:`|0\rangle` and :math:`|1\rangle` respectively. If ``"auto"``, will
use :func:`~.has_calibration_points` to determine if the data contains
calibration points.
Returns
-------
:class:`~.RamseyAnalysis`:
The instance of this analysis.
""" # NB the return type need to be specified manually to avoid circular import
self.artificial_detuning = artificial_detuning
self.qubit_frequency = qubit_frequency
return super().run(calibration_points=calibration_points)
[docs]
def run_fitting(self):
"""
Fits a :class:`~quantify_core.analysis.fitting_models.DecayOscillationModel`
to the data.
"""
model = fm.DecayOscillationModel()
time, data = self._choose_data_for_fit()
guess_pars = model.guess(data, t=time)
if self.calibration_points:
# if the data is on corrected axes certain parameters can be fixed
model.set_param_hint("offset", value=0.5, vary=False)
model.set_param_hint("amplitude", value=0.5, vary=False)
model.set_param_hint("phase", value=0, vary=False)
# this call provides updated guess_pars, model.guess is still needed.
guess_pars = model.make_params()
fit_result = model.fit(data, params=guess_pars, t=time)
self.fit_results.update({"Ramsey_decay": fit_result})
[docs]
def analyze_fit_results(self):
"""
Extract the real detuning and qubit frequency based on the artificial detuning
and fitted detuning.
"""
fit_warning = ba.check_lmfit(self.fit_results["Ramsey_decay"])
fit_parameters = self.fit_results["Ramsey_decay"].params
self.quantities_of_interest["T2*"] = ba.lmfit_par_to_ufloat(
fit_parameters["tau"]
)
self.quantities_of_interest["fitted_detuning"] = ba.lmfit_par_to_ufloat(
fit_parameters["frequency"]
)
self.quantities_of_interest["detuning"] = (
self.quantities_of_interest["fitted_detuning"] - self.artificial_detuning
)
if self.qubit_frequency is not None:
self.quantities_of_interest["qubit_frequency"] = (
self.qubit_frequency - self.quantities_of_interest["detuning"]
)
# If there is a problem with the fit, display an error message in the text box.
# Otherwise, display the parameters as normal.
if fit_warning is None:
self.quantities_of_interest["fit_success"] = True
text_msg = "Summary\n"
text_msg += format_value_string(
r"$T_2^*$",
self.quantities_of_interest["T2*"],
unit="s",
end_char="\n\n",
)
text_msg += format_value_string(
"artificial detuning",
self.artificial_detuning,
unit="Hz",
end_char="\n",
)
text_msg += format_value_string(
"fitted detuning",
self.quantities_of_interest["fitted_detuning"],
unit="Hz",
end_char="\n",
)
text_msg += format_value_string(
"actual detuning",
self.quantities_of_interest["detuning"],
unit="Hz",
end_char="\n",
)
if self.qubit_frequency is not None:
text_msg += "\n"
text_msg += format_value_string(
"initial qubit frequency",
self.qubit_frequency,
unit="Hz",
end_char="\n",
)
text_msg += format_value_string(
"fitted qubit frequency",
self.quantities_of_interest["qubit_frequency"],
unit="Hz",
)
else:
text_msg = ba.wrap_text(fit_warning)
self.quantities_of_interest["fit_success"] = False
self.quantities_of_interest["fit_msg"] = text_msg
[docs]
class AllXYAnalysis(SingleQubitTimedomainAnalysis):
"""
Normalizes the data from an AllXY experiment and plots against an ideal curve.
See section 2.3.2 of :cite:t:`reed_entanglement_2013` for an explanation of
the AllXY experiment and it's applications in diagnosing errors in single-qubit
control pulses.
"""
# pylint: disable=arguments-differ
[docs]
def run(self):
"""
Executes the analysis using specific datapoints as calibration points.
Returns
-------
:class:`~.AllXYAnalysis`:
The instance of this analysis.
""" # NB the return type need to be specified manually to avoid circular import
# The standard analysis of the AllXY analysis always uses datapoints measured
# within this experiment as calibration points.
return super().run(calibration_points=True)
# pylint: disable=arguments-differ
def _rotate_to_calibrated_axis(self):
if len(self.dataset.x0) == 21:
ref_idx_1 = 17
elif len(self.dataset.x0) == 21 * 2:
ref_idx_1 = 17 + 21
# use the values measured for II and XI as reference values.
super()._rotate_to_calibrated_axis(ref_idx_0=0, ref_idx_1=ref_idx_1)
[docs]
def process_data(self):
# Raise an exception if we do not have the correct number of points for a
# complete ALLXY experiment
number_points = len(self.dataset.x0)
if number_points % 21 != 0:
raise ValueError(
"Invalid dataset. The number of calibration points in an "
"AllXY experiment must be a multiple of 21."
)
super().process_data()
# add the ideal data as a reference curve
repeats = int(round(number_points / 21))
### Creating the ideal data ###
ideal_data = xr.DataArray(
data=np.concatenate(
(
0 * np.ones(5 * repeats),
0.5 * np.ones(12 * repeats),
np.ones(4 * repeats),
)
),
name="ideal_data",
coords={"x0": self.dataset_processed.coords["x0"]},
)
self.dataset_processed["ideal_data"] = ideal_data
### Analyzing Data ###
deviation = np.mean(np.abs(self.dataset_processed.pop_exc - ideal_data)).item()
self.quantities_of_interest["deviation"] = deviation
[docs]
class RabiAnalysis(SingleQubitTimedomainAnalysis):
"""
Fits a cosine curve to Rabi oscillation data and finds the qubit drive
amplitude required to implement a pi-pulse.
The analysis will automatically rotate the data so that the data lies along the
axis with the best SNR.
"""
[docs]
def run(self, calibration_points: bool = True):
"""
Parameters
----------
calibration_points
Specifies if the data should be rotated so that it lies along the axis with
the best SNR.
Returns
-------
:class:`~.RabiAnalysis`:
The instance of this analysis.
""" # NB the return type need to be specified manually to avoid circular import
# Override the `calibration_points="auto"`
if not isinstance(calibration_points, bool):
raise TypeError(
"Incorrect input. "
f"calibration_points={calibration_points} must be a bool."
)
return super().run(calibration_points=calibration_points)
# pylint: disable=arguments-differ
[docs]
def _rotate_to_calibrated_axis(self):
"""
If calibration points are True, automatically determine the point farthest
from the 0 point to use as a reference to rotate the data.
This will ensure the data lies along the axis with the best SNR.
"""
# index closest to rabi-pulse amplitude = 0
min_idx = abs(self.dataset_processed.x0.values).argmin()
# transmission measured for Rabi-pulse amplitude closest to 0
min_val = self.dataset_processed.S21[min_idx]
# find index with max absolute difference (distance in IQ space) to the S21_0
max_idx = abs(self.dataset_processed.S21 - min_val).argmax()
max_val = self.dataset_processed.S21[max_idx]
rotation_angle = np.angle(max_val - min_val)
rot_data = self.dataset_processed.S21 * np.exp(-1j * rotation_angle)
self.dataset_processed["S21_rot"] = rot_data
self.dataset_processed.S21_rot.attrs["name"] = "S21_rot"
self.dataset_processed.S21_rot.attrs["units"] = self.dataset.y0.units
self.dataset_processed.S21_rot.attrs[
"long_name"
] = "Rotated transmission $S_{21}^R$"
def _choose_data_for_fit(self):
if self.calibration_points:
y_data = self.dataset_processed.S21_rot.real.values
x_data = self.dataset_processed.x0.values
else:
# if the data is not rotated,fit the magnitude of the signal
y_data = np.abs(self.dataset_processed.S21.values)
x_data = self.dataset_processed.x0.values
return x_data, y_data
[docs]
def run_fitting(self):
"""
Fits a :class:`~quantify_core.analysis.fitting_models.RabiModel` to the data.
"""
model = fm.RabiModel()
drive_amplitude, data = self._choose_data_for_fit()
guess = model.guess(data, drive_amp=drive_amplitude)
fit_result = model.fit(data, params=guess, x=drive_amplitude)
self.fit_results.update({"Rabi_oscillation": fit_result})
[docs]
def analyze_fit_results(self):
"""
Checks fit success and populates :code:`.quantities_of_interest`.
"""
fit_result = self.fit_results["Rabi_oscillation"]
fit_warning = ba.wrap_text(ba.check_lmfit(fit_result))
# If there is a problem with the fit, display an error message in the text box.
# Otherwise, display the parameters as normal.
if fit_warning is None:
self.quantities_of_interest["fit_success"] = True
text_msg = "Summary\n"
text_msg += format_value_string(
"Pi-pulse amplitude",
fit_result.params["amp180"],
unit="V",
end_char="\n",
)
text_msg += format_value_string(
"Oscillation amplitude",
fit_result.params["amplitude"],
unit="V",
end_char="\n",
)
text_msg += format_value_string(
"Offset", fit_result.params["offset"], unit="V", end_char="\n"
)
else:
text_msg = ba.wrap_text(fit_warning)
self.quantities_of_interest["fit_success"] = False
self.quantities_of_interest["Pi-pulse amplitude"] = ba.lmfit_par_to_ufloat(
fit_result.params["amp180"]
)
self.quantities_of_interest["fit_msg"] = text_msg