基于量子纠缠的混沌域的自适应优化算法

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel self-adaptive optimization algorithm based on quantum entanglement, aiming to precisely compute the properties of chaotic systems and achieve superior optimization results. Traditional optimization methods often struggle with the complexity and non-linearity inherent in chaotic environments. We leverage the unique characteristics of quantum entanglement to enable a more precise and efficient optimization process. The core mechanism involves a quantum computation that accurately represents the evolution of the chaotic system, followed by a refined optimization step based on this representation. The algorithm's novelty lies in its ability to utilize entanglement to achieve a significantly improved accuracy compared to existing methods, particularly in challenging scenarios. This work presents a theoretical framework and preliminary implementation demonstrating the potential of this approach.

DOI: 10.5281/zenodo.22267414

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