# 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.
"""Cross resonance Hamiltonian tomography experiment analysis."""
from typing import List, Dict
import numpy as np
import qiskit_experiments.curve_analysis as curve
from qiskit_experiments.framework import AnalysisResultData
from qiskit_experiments.visualization import PlotStyle
[docs]
class CrossResonanceHamiltonianAnalysis(curve.CompositeCurveAnalysis):
r"""A class to analyze cross resonance Hamiltonian tomography experiment.
# section: fit_model
This analysis performs :class:`.BlochTrajectoryAnalysis` on the target qubit
with the control qubit states in :math:`\in \{ |0\rangle, |1\rangle \}`.
Based on the fit result, cross resonance Hamiltonian coefficients can be determined by
.. math::
ZX &= \frac{p_{x, |0\rangle} - p_{x, |1\rangle}}{2}, \\
ZY &= \frac{p_{y, |0\rangle} - p_{y, |1\rangle}}{2}, \\
ZZ &= \frac{p_{z, |0\rangle} - p_{z, |1\rangle}}{2}, \\
IX &= \frac{p_{x, |0\rangle} + p_{x, |1\rangle}}{2}, \\
IY &= \frac{p_{y, |0\rangle} + p_{y, |1\rangle}}{2}, \\
IZ &= \frac{p_{z, |0\rangle} + p_{z, |1\rangle}}{2},
where :math:`p_{\beta, |j\rangle}` is a fit parameter of :class:`.BlochTrajectoryAnalysis`
for the projection axis :math:`\beta` with the control qubit state :math:`|j\rangle`.
"""
def __init__(self):
analyses = []
for control_state in (0, 1):
analysis = curve.BlochTrajectoryAnalysis(name=f"ctrl{control_state}")
analysis.set_options(filter_data={"control_state": control_state})
analyses.append(analysis)
super().__init__(analyses=analyses)
@classmethod
def _default_options(cls):
"""Return the default analysis options."""
default_options = super()._default_options()
default_options.plotter.set_options(
subplots=(3, 1),
style=PlotStyle(
{
"figsize": (8, 10),
"legend_loc": "lower right",
"textbox_rel_pos": (0.28, -0.10),
}
),
)
default_options.plotter.set_figure_options(
xlabel="Flat top width",
ylabel=[
r"$\langle$X(t)$\rangle$",
r"$\langle$Y(t)$\rangle$",
r"$\langle$Z(t)$\rangle$",
],
xval_unit="s",
ylim=(-1, 1),
series_params={
"x_ctrl0": {
"canvas": 0,
"color": "blue",
"label": "X (ctrl0)",
"symbol": "o",
},
"y_ctrl0": {
"canvas": 1,
"color": "blue",
"label": "Y (ctrl0)",
"symbol": "o",
},
"z_ctrl0": {
"canvas": 2,
"color": "blue",
"label": "Z (ctrl0)",
"symbol": "o",
},
"x_ctrl1": {
"canvas": 0,
"color": "red",
"label": "X (ctrl1)",
"symbol": "^",
},
"y_ctrl1": {
"canvas": 1,
"color": "red",
"label": "Y (ctrl1)",
"symbol": "^",
},
"z_ctrl1": {
"canvas": 2,
"color": "red",
"label": "Z (ctrl1)",
"symbol": "^",
},
},
)
return default_options
def _create_analysis_results(
self,
fit_data: Dict[str, curve.CurveFitResult],
quality: str,
**metadata,
) -> List[AnalysisResultData]:
outcomes = []
for control in ("z", "i"):
for target in ("x", "y", "z"):
p0_val = fit_data["ctrl0"].ufloat_params[f"p{target}"]
p1_val = fit_data["ctrl1"].ufloat_params[f"p{target}"]
if control == "z":
coef_val = 0.5 * (p0_val - p1_val) / (2 * np.pi)
else:
coef_val = 0.5 * (p0_val + p1_val) / (2 * np.pi)
outcomes.append(
AnalysisResultData(
name=f"omega_{control}{target}",
value=coef_val,
quality=quality,
extra={
"unit": "Hz",
**metadata,
},
)
)
return outcomes