基于动态量子态的自适应算法设计

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper introduces a novel algorithm based on dynamic quantum state evolution, designed to enhance algorithm efficiency and accuracy through automated parameter adjustment. Traditional parameter adjustment methods often lack the ability to fully exploit the inherent dynamic behavior of quantum states. This algorithm leverages quantum state evolution to dynamically adjust algorithm parameters, enabling a more adaptive and robust approach to optimization. The core mechanism involves a continuous feedback loop that monitors and modifies the quantum state, optimizing the algorithm's performance in real-time. We demonstrate the effectiveness of this approach through a series of benchmark problems, showcasing significant improvements in both computational speed and solution accuracy compared to traditional methods. The proposed algorithm offers a promising avenue for accelerating complex optimization tasks in various fields.

DOI: 10.5281/zenodo.22154326

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