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.
翻译:量子计算的出现使研究人员能够对许多现有研究应用量子电路。 利用量子电路和量子差异编程,开展了许多研究,例如“ 量子强化机器学习” (QML) 。 特别是, 量子强化学习是测试量子机器学习可能性的良好领域, 并且正在进行大量研究。 这项工作将引入使用量子强化学习的概念, 并通过实施和实验确认其可能性。 我们将首先介绍量子强化学习的背景知识和工作原则, 然后用 PennyLane 图书馆指导实施方法。 我们还将讨论从通过这项工作获得的实验结果中学习量子强化的力度和可能性。