基于量子机器学习的动态系统优化

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel dynamic system optimization algorithm based on quantum machine learning. Traditional optimization methods often struggle with complex systems exhibiting dynamic relationships, necessitating static approaches. Quantum machine learning offers the potential for enhanced adaptability and efficiency. This work proposes a quantum-enhanced framework for learning and optimizing the dependencies between system elements, utilizing a dynamic representation of the system through graph transformations. The core mechanism leverages quantum entanglement and superposition to facilitate rapid exploration of the system's state space, enabling faster convergence and improved solution quality compared to classical methods. This paper details the proposed algorithm, its underlying quantum mechanics, and preliminary experimental results demonstrating its effectiveness in optimizing a simulated system exhibiting complex dynamics.

DOI: 10.5281/zenodo.22263896

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