TY - GEN
T1 - Introduction to Quantum Reinforcement Learning
T2 - 12th International Conference on Information and Communication Technology Convergence, ICTC 2021
AU - Kwak, Yunseok
AU - Yun, Won Joon
AU - Jung, Soyi
AU - Kim, Jong Kook
AU - Kim, Joongheon
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by the National Research Foundation of Korea (2019M3E4A1080391). Joongheon Kim is a corresponding author of this paper.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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 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.
AB - 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 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.
UR - http://www.scopus.com/inward/record.url?scp=85122924506&partnerID=8YFLogxK
U2 - 10.1109/ICTC52510.2021.9620885
DO - 10.1109/ICTC52510.2021.9620885
M3 - Conference contribution
AN - SCOPUS:85122924506
T3 - International Conference on ICT Convergence
SP - 416
EP - 420
BT - ICTC 2021 - 12th International Conference on ICT Convergence
PB - IEEE Computer Society
Y2 - 20 October 2021 through 22 October 2021
ER -