Quantum Adaptive Machine Learning Reinforcement Learning Algorithm

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum Adaptive Machine Learning Reinforcement Learning Algorithm is a novel approach to reinforcement learning that leverages the unique properties of quantum mechanics, specifically quantum superposition and entanglement, to enhance algorithm adaptability and efficiency. The core mechanism centers around utilizing quantum state manipulation to facilitate exploration and learning in complex environments. This research proposes a new algorithm architecture, focusing on the integration of quantum-enhanced randomness and optimization strategies, aiming to surpass the limitations of classical reinforcement learning techniques. The paper provides a detailed explanation of the proposed algorithm, its mathematical foundations, and preliminary experimental results demonstrating its potential for improved performance in challenging scenarios. We explore the benefits of quantum-induced exploration, which could lead to faster convergence and the ability to tackle intricate problem domains. The paper concludes with a discussion of future research directions and potential applications.

DOI: 10.5281/zenodo.22298854

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