用量子计算提升强化学习，探索新算法可能。
An Introduction to Quantum Reinforcement Learning (QRL)
- 结合量子计算原理改进经典强化学习算法
- 为复杂决策问题提供潜在加速路径
- 适合对量子机器学习感兴趣的科研人员
量子计算（QC）与机器学习（ML）的最新进展引发了这两个前沿领域融合的广泛关注。在各类机器学习技术中，强化学习（RL）因其解决复杂序列决策问题的能力而尤为突出，已在经典机器学习领域取得显著成功。如今，新兴的量子强化学习（QRL）旨在通过融入量子计算原理来增强强化学习算法。本文为更广泛的人工智能与机器学习社区提供了该激动人心领域的入门介绍。
原文摘要 · Abstract (English)
Recent advancements in quantum computing (QC) and machine learning (ML) have sparked considerable interest in the integration of these two cutting-edge fields. Among the various ML techniques, reinforcement learning (RL) stands out for its ability to address complex sequential decision-making problems. RL has already demonstrated substantial success in the classical ML community. Now, the emerging field of Quantum Reinforcement Learning (QRL) seeks to enhance RL algorithms by incorporating principles from quantum computing. This paper offers an introduction to this exciting area for the broader AI and ML community.
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