Title: Quantum Bayesian Network Optimization (QBNO)

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum Bayesian Network Optimization (QBNO) presents a novel approach to optimization problems, leveraging the power of Bayesian networks to dynamically adjust decision-making processes in the presence of uncertainty and feedback. This paper introduces a new architecture designed to enhance the robustness and adaptability of Bayesian networks, allowing them to continuously refine their parameters based on evolving system data. The core mechanism involves a dynamically updated Bayesian network that incorporates real-time uncertainty and feedback, leading to improved performance across a range of complex decision-making scenarios. We demonstrate the efficacy of our approach through a series of simulations and analyses, showcasing its potential to surpass existing optimization techniques.

DOI: 10.5281/zenodo.22195976

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