Adaptive Bayesian Networks: A Quantumized Algorithm for Complex System Modeling

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

The Adaptive Bayesian Networks (ABN) represent a powerful framework for modeling complex systems by leveraging probabilistic inference and Bayesian reasoning. This paper introduces a novel quantumized algorithm for ABN, designed to enhance learning and prediction accuracy, particularly for systems exhibiting intricate probability distributions. We explore the utilization of quantum phenomena – superposition and entanglement – to accelerate the network's learning process and improve the precision of risk assessments. The core mechanism focuses on applying quantum-enhanced optimization techniques to the ABN structure, leading to a more efficient and accurate representation of the underlying probability landscape. This research addresses the limitations of classical ABN algorithms, offering a promising pathway toward enhanced predictive capabilities in diverse domains.

DOI: 10.5281/zenodo.22183735

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