Title: Quantum Bayesian Network for Parameter Estimation in Complex Systems

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

Quantum Bayesian Networks (QBNs) offer a novel approach to parameter estimation in complex systems, particularly those exhibiting probabilistic dependencies and inherent uncertainty. This paper explores the development of a QBN specifically designed to explicitly model quantum uncertainty, aiming to improve the accuracy of parameter estimation compared to traditional Bayesian networks. We introduce a mechanism based on quantum measurements to generate probabilistic relationships between parameters, thereby capturing the inherent uncertainty present in quantum systems. The resulting QBN demonstrates enhanced performance in scenarios requiring precise parameter determination, particularly in systems with complex dependencies and noisy data. The core mechanism leverages the principles of quantum measurement to generate probabilistic relationships, offering a pathway towards a more robust and accurate representation of uncertainty.

DOI: 10.5281/zenodo.22472696

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