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

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# (C) Copyright IBM 2021.
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# This code is licensed under the Apache License, Version 2.0. You may
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"""Cross resonance Hamiltonian tomography experiment analysis."""

from typing import List, Dict
import numpy as np

from qiskit.utils.deprecation import deprecate_func

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`. """ @deprecate_func( since="0.8", package_name="qiskit-experiments", additional_msg=( "Due to the deprecation of Qiskit Pulse, experiments and related classses " "involving pulse gate calibrations like this one have been deprecated." ), ) 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