基于自适应量子势场的优化算法

Jincheng Zhang

Published Sep 6, 2026• 0 citations

Abstract

The development of efficient quantum algorithms is critically dependent on the ability to precisely control and optimize quantum systems. Traditional approaches to quantum system optimization often rely on static potentials, limiting their adaptability and potential for improvement. This paper introduces a novel optimization algorithm based on dynamically adjusting quantum potentials, leveraging the inherent change in quantum environments to achieve enhanced performance and efficiency. We detail the core mechanism, outlining the iterative process of characterizing, adjusting, and analyzing the quantum potential field to guide the optimization of quantum states. The resulting algorithm demonstrates improved performance compared to existing methods across a range of quantum tasks, offering a significant step forward in the field of quantum computation.

DOI: 10.5281/zenodo.22509998

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