A Fast and Stable Marginal-Likelihood Calibration Method with Application to Quantum Characterization

• URL: http://arxiv.org/abs/2308.12552v2
• Date: Mon, 09 Dec 2024 17:08:50 GMT
• Title: A Fast and Stable Marginal-Likelihood Calibration Method with Application to Quantum Characterization
• Authors: Mohammad Motamed, N. Anders Petersson,
• Abstract summary: We present a marginal likelihood strategy integrated into the Kennedy-O'Hagan (KOH) Bayesian framework.<n>The proposed method is both computationally efficient and numerically stable, even in large dataset regimes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present a marginal likelihood strategy integrated into the Kennedy-O'Hagan (KOH) Bayesian framework, where a Gaussian process serves as a prior for model discrepancy. The proposed method is both computationally efficient and numerically stable, even in large dataset regimes where the likelihood function approaches degeneracy. Applied to the characterization of a super-conducting quantum device at Lawrence Livermore National Laboratory, the approach enhances the predictive accuracy of the Lindblad master equations for modeling Ramsey measurement data by effectively quantifying uncertainties consistent with the quantum data

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