基于自适应量子算法的非线性优化

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper presents a novel approach to non-linear optimization utilizing a dynamically adjusted quantum algorithm. Traditional optimization methods often rely on manual parameter tuning, limiting flexibility and potentially hindering performance. Our proposed method employs a self-adaptive quantum algorithm, which continuously monitors and adjusts quantum parameters to optimize the solution process. The core mechanism involves a closed-loop optimization where the quantum algorithm's parameters are modified based on the observed solution quality and the system's current state. This adaptive approach enhances optimization accuracy, reduces computational cost, and facilitates the handling of complex, non-linear problems, offering a significant advancement over traditional methods. The paper details the algorithm's architecture, experimental validation, and analysis of its performance, showcasing its potential for improved optimization across diverse applications.

DOI: 10.5281/zenodo.22265212

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