基于量子图的自适应量子算法优化

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper presents a novel quantum algorithm optimization approach utilizing a dynamically adjusted quantum graph. Traditional optimization methods often rely on manual parameter tuning, limiting the algorithm's adaptability. This work proposes a quantum graph-based framework where the graph itself evolves during the optimization process, allowing for continuous refinement and improved performance. We demonstrate the effectiveness of this method through a series of benchmark quantum algorithms, showcasing its ability to achieve state-of-the-art results with reduced computational cost. The core mechanism leverages the inherent properties of quantum graphs to dynamically adjust path lengths and interactions, ultimately leading to enhanced algorithm efficiency.

DOI: 10.5281/zenodo.22270568

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