Quantum-Enhanced Bayesian Inference for Complex Networks

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper investigates the potential of quantum computing to revolutionize Bayesian inference applied to complex networks. We propose a novel Bayesian inference algorithm, termed a 'Quantum Bayesian Network' (QBN), that integrates quantum mechanics principles to accelerate the identification of key nodes and connections within these networks. The core mechanism centers around leveraging quantum superposition and entanglement to enhance the probabilistic nature of Bayesian inference, leading to improved accuracy and efficiency. The proposed QBN offers a fundamentally different approach compared to traditional Bayesian methods, potentially unlocking new insights into network structure and dynamics. This work explores the theoretical foundations, potential implementation challenges, and preliminary results demonstrating the feasibility of this quantum-enhanced approach.

DOI: 10.5281/zenodo.22154847

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