基于自适应的量子算法优化策略

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

This paper proposes a novel quantum algorithm optimization strategy that leverages adaptive learning algorithms to dynamically adjust quantum algorithm parameters. Traditional approaches often rely on manual parameter tuning, which can be time-consuming and suboptimal. Our method aims to improve quantum algorithm efficiency and accuracy by automatically optimizing these parameters based on the input data. We present a framework that incorporates a self-learning mechanism to adaptively adjust parameters, offering a significant improvement over existing methods. The core of our approach involves a dynamic parameter adjustment process guided by a learned model that constantly evaluates the algorithm's performance. We demonstrate the effectiveness of this strategy through theoretical analysis and simulations, showcasing its potential to accelerate algorithm execution and enhance results.

DOI: 10.5281/zenodo.22522174

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