基于自适应的量子系统优化算法

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum systems represent a complex landscape for optimization, particularly in areas like materials science, drug discovery, and financial modeling. However, achieving optimal solutions often requires extensive computational resources and sensitive to fluctuations in quantum states. This paper introduces a novel algorithm, "Adaptive Quantum Optimization," designed to address these challenges by dynamically adjusting quantum step sizes during the optimization process. The core mechanism of this algorithm leverages self-adaptive quantum algorithms to optimize the quantum trajectory, enhancing both algorithm efficiency and stability. We demonstrate the effectiveness of this approach through simulations and preliminary experiments, achieving significant improvements in convergence speed and robustness compared to traditional optimization methods. This work presents a promising pathway toward more efficient and reliable quantum optimization.

DOI: 10.5281/zenodo.22221693

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