Title: Quantum-Accelerated Bayesian Network Propagation

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

The propagation of Bayesian network (BN) predictions is a crucial task in various fields, from medical diagnosis to risk assessment. Classical methods often struggle with large networks due to computational complexity and convergence time limitations. This paper explores a novel quantum-accelerated Bayesian network propagation method leveraging quantum annealing and variational quantum algorithms. We demonstrate a significant improvement in convergence speed and accuracy compared to existing approaches, offering a fundamentally more efficient pathway for handling complex Bayesian network models. The core mechanism focuses on utilizing quantum annealing to effectively explore the network's state space, accelerating the propagation process and reducing computational costs. This research aims to unlock the potential of quantum computing for enhanced Bayesian network modeling.

DOI: 10.5281/zenodo.22199947

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