Title: Quantum-Enhanced Bayesian Network Inference

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

Quantum-enhanced Bayesian Network Inference addresses the computational challenges associated with large, complex Bayesian networks through the integration of quantum algorithms. This paper introduces a novel technique leveraging Grover's search to accelerate conditional probability computations, ultimately enhancing inference efficiency. The core mechanism centers around quantum-enhanced probability updates, offering a potential paradigm shift in the processing of large network data. We explore the theoretical implications and preliminary experimental results demonstrating the efficacy of this approach, highlighting its potential to overcome limitations of traditional methods. The paper concludes with a discussion of future research directions, focusing on scalability and practical application.

DOI: 10.5281/zenodo.22155825

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