基于量子态的动态概率分布优化算法

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper presents a novel dynamic probability distribution optimization algorithm based on quantum state evolution. Traditional probabilistic distribution optimization methods often rely on static parameter adjustments, limiting their effectiveness and generalization capabilities. We propose an algorithm that leverages the inherent quantum properties of probability distributions – specifically, their ability to adapt and evolve – to dynamically adjust the distribution parameters during the optimization process. The algorithm's core mechanism involves a quantum state representation, a feedback loop based on the state's evolution, and a controlled parameter update strategy. We demonstrate the algorithm's superiority through theoretical analysis and extensive numerical experiments, showcasing significant improvements in optimization performance and robustness compared to existing methods. The core claim is that the algorithm's quantum state adaptation allows for more efficient and reliable optimization compared to traditional approaches.

DOI: 10.5281/zenodo.22634309

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