基于自相关性的量子态的自适应算法

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel quantum state-based adaptive algorithm designed to optimize complex algorithms. Traditional algorithm parameter adjustment methods are often static and lack the flexibility to adapt to dynamic system states and target algorithm parameters. This research proposes a quantum-mechanical approach that leverages the inherent quantum state properties – specifically, self-correlation – to dynamically adjust algorithm parameters, leading to improved performance compared to traditional optimization techniques. The core mechanism centers around leveraging these self-correlations to create a continuous, adaptive optimization process. This approach has the potential to significantly enhance the efficiency of various algorithms across diverse computational domains.

DOI: 10.5281/zenodo.22279096

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