An Introduction to Quantum Reinforcement Learning (QRL)
It provides an introductory overview for the AI and ML community, but is incremental as it does not present new results or methods.
This paper introduces Quantum Reinforcement Learning (QRL) as an emerging field that aims to enhance reinforcement learning algorithms by incorporating principles from quantum computing, targeting complex sequential decision-making problems.
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.