基于量子纠缠的概率模型优化

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel probabilistic model optimization technique based on quantum entanglement. Traditional optimization methods often rely on static models, while this approach leverages the unique properties of quantum entanglement to refine probability distributions, resulting in improved prediction accuracy. The core mechanism involves creating entangled quantum states to represent probability distributions, allowing for a more nuanced and accurate optimization process. This work explores the potential of quantum entanglement to enhance probabilistic modeling, offering a fundamentally new approach to optimization.

DOI: 10.5281/zenodo.22509710

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