基于量子势的动态算法

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper introduces a novel dynamic algorithm based on quantum potential, designed to autonomously adjust algorithm strategies in response to fluctuating quantum potentials. Traditional algorithms often rely on static strategies, limiting their adaptability to dynamic environments. Our approach leverages quantum potential to continuously evaluate and refine the algorithm's parameters, optimizing performance across a wider range of input data. We present a framework incorporating quantum state manipulation and gradient-based optimization, demonstrating a significant improvement in algorithm efficiency compared to static methods. This research explores the potential of quantum mechanics to enhance optimization processes, offering a promising avenue for advancing machine learning and computational optimization within various domains.

DOI: 10.5281/zenodo.22517608

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