基于量子自适应的概率分布优化

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper presents a novel probabilistic distribution optimization algorithm based on quantum self-adaptation. Traditional methods rely on manual parameter adjustments, while quantum self-adaptation leverages quantum entanglement and quantum computation to dynamically optimize probability distributions, leading to enhanced prediction accuracy. The core mechanism involves manipulating a quantum system through quantum entanglement and quantum computation to adjust key parameters, ultimately improving the accuracy of probabilistic models. This work demonstrates the potential of this approach to outperform traditional optimization techniques in complex scenarios, offering a promising pathway toward more robust and efficient probabilistic modeling.

DOI: 10.5281/zenodo.22329739

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