Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementation
It provides an introductory guide for researchers interested in applying quantum computing to reinforcement learning, but it is incremental as it builds on existing quantum machine learning concepts.
This paper introduces quantum reinforcement learning using variational quantum circuits and demonstrates its feasibility through implementation and experimentation with the PennyLane library.
The emergence of quantum computing enables for researchers to apply quantum circuit on many existing studies. Utilizing quantum circuit and quantum differential programming, many research are conducted such as \textit{Quantum Machine Learning} (QML). In particular, quantum reinforcement learning is a good field to test the possibility of quantum machine learning, and a lot of research is being done. This work will introduce the concept of quantum reinforcement learning using a variational quantum circuit, and confirm its possibility through implementation and experimentation. We will first present the background knowledge and working principle of quantum reinforcement learning, and then guide the implementation method using the PennyLane library. We will also discuss the power and possibility of quantum reinforcement learning from the experimental results obtained through this work.