  Addressing the paramount need for precise calibration in superconducting
quantum qubits, especially in frequency control, this study introduces a novel
calibration scheme harnessing the principles of Many-Body Localization (MBL).
While existing strategies, such as Google's snake algorithm, have targeted
optimization of qubit frequency parameters, our MBL-based methodology emerges
as a stalwart against noise, notably crosstalk and residual coupling errors,
thereby significantly enhancing quantum processor fidelity and stability
without necessitating extensive optimization computation. Not only does this
approach provide a marked improvement in performance, particularly where
specific residue couplings are present, but it also presents a more
resource-efficient and cost-effective calibration process. The research
delineated herein affords fresh insights into advanced calibration strategies
and propels forward the domain of superconducting quantum computation by
offering a robust framework for future explorations in minimizing error and
optimizing qubit performance.
