Quantum Bayesian Networks for Probabilistic Constraint Satisfaction

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum Bayesian Networks (QBNs) represent a novel approach to solving probabilistic constraint satisfaction problems (CSP) by incorporating quantum Bayesian principles. This paper explores the potential of QBNs to enhance the robustness and adaptability of CSP solvers, offering a more efficient and potentially superior solution compared to traditional methods. We detail a novel Bayesian Network architecture that utilizes variational inference and probabilistic representation of uncertainty to dynamically update belief states during optimization, resulting in improved solution quality. The core mechanism centers around automatically adjusting belief states based on the progress of the optimization process. We present a detailed theoretical analysis and preliminary experimental results demonstrating the efficacy of this approach.

DOI: 10.5281/zenodo.22471942

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