基于量子纠缠的概率图推理算法

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper introduces a novel probabilistic graph reasoning algorithm based on quantum entanglement. Traditional graph algorithms often struggle with large-scale graph data due to computational complexity. This work proposes a quantum-enhanced probabilistic graph reasoning framework leveraging quantum entanglement to accelerate inference speed and improve the handling of complex graph relationships. We detail the core mechanism, including the entanglement-based probabilistic representation and a quantum-enhanced optimization strategy, demonstrating its efficacy through simulations and preliminary experimental results. The algorithm's potential to address the limitations of classical graph algorithms is discussed, emphasizing its advantages for scenarios involving high-dimensional graphs and complex relationships.

DOI: 10.5281/zenodo.22225121

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