Bayesian Quantum Convergence

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper explores a novel Bayesian framework for assessing the convergence of quantum algorithms. Traditional convergence criteria often rely on fixed, computationally intensive checks, failing to adequately handle the inherent stochasticity of quantum systems. We propose a dynamic, probabilistic state space, incorporating a 'Quantum Drift' algorithm, to continuously update the system's state as it evolves, providing a more robust and adaptable approach to convergence analysis. The framework leverages Bayesian principles to quantify uncertainty and guide algorithm refinement. The core claim is that this approach offers a fundamentally new method for understanding and improving the convergence of quantum algorithms, particularly those involving complex quantum systems.

DOI: 10.5281/zenodo.22299050

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