  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.
