基于量子纠缠的多层概率推理

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper introduces a novel approach to probabilistic reasoning leveraging the unique properties of quantum entanglement. We propose a multi-layered probabilistic inference system constructed upon a quantum entanglement-based probabilistic graphical model. This model utilizes entangled nodes to connect variables across different layers, enabling efficient multi-level reasoning. The core mechanism involves employing quantum gate operations for probability updates and inference, capitalizing on the inherent parallelism offered by quantum entanglement. Our approach addresses the limitations of traditional probabilistic reasoning in handling high-dimensional, non-linear data, aiming to significantly improve both inference speed and accuracy. The design focuses on creating a scalable model that can potentially unlock new possibilities in areas such as complex data analysis, machine learning, and optimization problems where high-dimensional dependencies are prevalent. The theoretical framework and the proposed model are presented, outlining the key components and potential benefits. We demonstrate the feasibility of this approach and highlight the potential for future research and development in this area.

DOI: 10.5281/zenodo.22152276

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