Adaptive Quantum Environment Reinforcement Learning

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper introduces an adaptive quantum environment reinforcement learning framework, aiming to achieve precise modeling and prediction of quantum system behavior. Traditional reinforcement learning relies on static environments, limiting its applicability to complex systems. Our proposed method dynamically adjusts the quantum environment to guide the system towards desired states, offering a significant improvement in performance compared to conventional approaches. We detail the core mechanism, utilizing quantum environment dynamics to create a self-optimizing learning process. The study addresses the limitations of traditional reinforcement learning by incorporating a dynamic environment, leading to enhanced precision and adaptability. This work presents a novel framework with potential applications in diverse quantum computing tasks and simulations.

DOI: 10.5281/zenodo.22199273

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