基于自适应量子势场的量子算法优化

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper presents a novel quantum algorithm optimization approach based on a dynamically adjusted quantum potential field. Traditional quantum algorithm optimization often relies on static parameter adjustments, which can limit the potential for significant improvements. This work introduces a new method that leverages the inherent dynamic nature of the quantum potential field to dynamically adjust parameters, thereby enhancing the performance of quantum algorithms. The core mechanism involves analyzing the quantum potential field and iteratively adjusting its parameters to optimize the algorithm's output. This dynamic adjustment allows for a more efficient and robust optimization process compared to static methods. We demonstrate the efficacy of this approach through a series of simulations and analyses, showcasing significant performance gains in specific benchmark quantum algorithms. The resulting algorithm offers a pathway to more effective quantum algorithm design and optimization.

DOI: 10.5281/zenodo.22622772

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