基于自适应量子路径的量子算法优化策略

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel quantum algorithm optimization strategy based on dynamically adjusted quantum paths. Traditional quantum algorithm optimization often relies on static parameter adjustments, limiting the potential for significant efficiency gains. This work proposes a dynamic approach that intelligently adjusts quantum path parameters during the execution of a quantum algorithm, optimizing both the algorithm's efficiency and cost. We demonstrate the effectiveness of this method through a series of simulations and analysis, showcasing a substantial reduction in computational resources compared to conventional optimization techniques. The core mechanism involves a self-adaptive parameter tuning process driven by a feedback loop, continuously refining the quantum path to achieve optimal performance. The resulting algorithm exhibits improved performance metrics across a range of quantum algorithms, highlighting the advantages of this adaptive approach.

DOI: 10.5281/zenodo.22274253

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