基于量子自适应的优化算法框架

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper introduces a novel optimization algorithm framework leveraging quantum self-adaptation. Traditional optimization methods rely on manual parameter tuning, lacking inherent adaptability. Our approach utilizes quantum self-adaptation, introducing the concept of "quantum optimization" to dynamically adjust optimization parameters and enhance algorithmic efficiency. We demonstrate the effectiveness of this framework through a series of simulations, showcasing improved performance on a benchmark set of optimization problems. The core mechanism involves employing quantum-enhanced gradient estimation and adaptive parameter adjustment, resulting in a more robust and efficient optimization process. The paper concludes with a discussion of the potential implications and future research directions of this innovative approach.

DOI: 10.5281/zenodo.22325981

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