基于量子机器学习的非线性强化学习的自适应算法

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper presents a novel approach to reinforcement learning, leveraging the unique capabilities of quantum machine learning to develop an adaptive learning algorithm. Traditional reinforcement learning algorithms often struggle with complex environments due to the computational limitations of classical systems. We propose a quantum-enhanced learning framework that dynamically adjusts the learning strategy based on environmental changes, resulting in enhanced adaptation and exploration. This paper explores the potential of quantum mechanics to accelerate learning and improve the ability to navigate challenging environments. The core mechanism focuses on utilizing quantum entanglement and superposition to represent and update the state space more efficiently, facilitating faster convergence and the discovery of optimal policies. The proposed algorithm demonstrates promising results across a range of scenarios, showcasing its potential to overcome the limitations of classical reinforcement learning.

DOI: 10.5281/zenodo.22203305

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