基于动态概率场自适应的量子算法优化

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper introduces a novel quantum algorithm optimization approach leveraging dynamic probability field adaptation. Traditional quantum algorithm optimization methods often rely on static models, struggling to effectively address complex systems exhibiting dynamic variations in the system. Our method proposes a self-adaptive probability field framework, dynamically adjusting probability field parameters to achieve global optimization and global optimal solutions for quantum algorithms. We detail the core mechanism, highlighting how this adaptation mitigates the limitations of static optimization techniques. The paper concludes with a discussion of the potential benefits and challenges of this approach, emphasizing its capacity to improve the performance and robustness of quantum algorithms.

DOI: 10.5281/zenodo.22158522

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