Quantum Bayesian Networks with Dynamic Kernel Calibration

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum Bayesian Networks (QBNs) offer a promising framework for probabilistic reasoning, particularly in complex systems with inherent uncertainty. Traditional QBNs rely on fixed, static kernels, which can struggle to effectively handle noisy data and evolving patterns. This paper introduces a novel approach to QBN modeling that incorporates a dynamic kernel calibration mechanism, enabling the network to adapt to observed uncertainty and improve predictive accuracy. We demonstrate the effectiveness of this approach through simulations and preliminary experiments, showcasing its ability to outperform existing methods in scenarios with high noise levels and complex dependencies.

DOI: 10.5281/zenodo.22280690

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