基于自适应量子模型的强化学习

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces a novel reinforcement learning framework based on adaptive quantum models, leveraging the inherent quantum state adaptability to achieve enhanced learning efficiency and precision. Traditional reinforcement learning often relies on fixed parameters, limiting the potential for optimization. Our approach utilizes quantum state dynamics to dynamically adjust the learning process, fostering a more effective and accurate training regime. The core claim is that this framework offers a significant improvement over existing methods through the integration of quantum state adaptation. We demonstrate the effectiveness of this design through a series of simulations, showcasing enhanced convergence speed and improved performance on a challenging reinforcement learning task. The paper concludes with a discussion of the potential applications and future directions of this research.

DOI: 10.5281/zenodo.22278745

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