Quantum Optimization Algorithm Adjustment

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper presents a novel approach to quantum optimization algorithms, introducing an adaptive adjustment mechanism that dynamically adjusts algorithm parameters based on environmental changes. Traditional optimization techniques often rely on manual parameter design, limiting their adaptability. Our proposed mechanism leverages the inherent capabilities of quantum optimization algorithms to continuously refine parameters, resulting in significantly improved performance across a range of scenarios. The paper details the core concept, mechanism implementation, and preliminary results demonstrating the effectiveness of this adaptive adjustment in various applications. We demonstrate that this method enhances algorithm robustness and responsiveness to environmental shifts.

DOI: 10.5281/zenodo.22263553

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