Quantum Reinforcement Learning for Coordinated Satellite Systems

Gyu Seon Kim; Samuel Yen Chi Chen; Soohyun Park; Joongheon Kim

doi:10.1109/ICASSP49660.2025.10889145

Quantum Reinforcement Learning for Coordinated Satellite Systems

Gyu Seon Kim
, Samuel Yen Chi Chen
, Soohyun Park^*
, Joongheon Kim^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Citations (Scopus)

Abstract

Reinforcement learning (RL) using conventional neural networks (NN) has significantly progressed in various applications. However, conventional RL needs help training in environments with large-scale action dimensions, such as coordinated mobility/satellite systems. Quantum reinforcement learning (QRL) with quantum NN (QNN) can address this problem through superposition and entanglement, one of the great features of quantum mechanics. Based on its 'i) fast convergence' and 'ii) high scalability', unique advantages of QRL that distinguish it from conventional RL, this paper highlights the potential for QRL utilization in coordinated mobility and satellite systems.

Original language	English
Title of host publication	2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 - Proceedings
Editors	Bhaskar D Rao, Isabel Trancoso, Gaurav Sharma, Neelesh B. Mehta
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350368741
DOIs	https://doi.org/10.1109/ICASSP49660.2025.10889145
Publication status	Published - 2025
Event	2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 - Hyderabad, India Duration: 2025 Apr 6 → 2025 Apr 11

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)	1520-6149

Conference

Conference	2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025
Country/Territory	India
City	Hyderabad
Period	25/4/6 → 25/4/11

Bibliographical note

Publisher Copyright:
© 2025 IEEE.

Keywords

Mobility/Satellite Systems
Quantum Neural Network (QNN)
Quantum Reinforcement Learning (QRL)

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP49660.2025.10889145

Cite this

Kim, G. S., Chen, S. Y. C., Park, S., & Kim, J. (2025). Quantum Reinforcement Learning for Coordinated Satellite Systems. In B. D. Rao, I. Trancoso, G. Sharma, & N. B. Mehta (Eds.), 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 - Proceedings (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP49660.2025.10889145

Quantum Reinforcement Learning for Coordinated Satellite Systems. / Kim, Gyu Seon; Chen, Samuel Yen Chi; Park, Soohyun et al.
2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 - Proceedings. ed. / Bhaskar D Rao; Isabel Trancoso; Gaurav Sharma; Neelesh B. Mehta. Institute of Electrical and Electronics Engineers Inc., 2025. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kim, GS, Chen, SYC, Park, S & Kim, J 2025, Quantum Reinforcement Learning for Coordinated Satellite Systems. in BD Rao, I Trancoso, G Sharma & NB Mehta (eds), 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 - Proceedings. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025, Hyderabad, India, 25/4/6. https://doi.org/10.1109/ICASSP49660.2025.10889145

Kim GS, Chen SYC, Park S, Kim J. Quantum Reinforcement Learning for Coordinated Satellite Systems. In Rao BD, Trancoso I, Sharma G, Mehta NB, editors, 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2025. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP49660.2025.10889145

Kim, Gyu Seon ; Chen, Samuel Yen Chi ; Park, Soohyun et al. / Quantum Reinforcement Learning for Coordinated Satellite Systems. 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 - Proceedings. editor / Bhaskar D Rao ; Isabel Trancoso ; Gaurav Sharma ; Neelesh B. Mehta. Institute of Electrical and Electronics Engineers Inc., 2025. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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abstract = "Reinforcement learning (RL) using conventional neural networks (NN) has significantly progressed in various applications. However, conventional RL needs help training in environments with large-scale action dimensions, such as coordinated mobility/satellite systems. Quantum reinforcement learning (QRL) with quantum NN (QNN) can address this problem through superposition and entanglement, one of the great features of quantum mechanics. Based on its 'i) fast convergence' and 'ii) high scalability', unique advantages of QRL that distinguish it from conventional RL, this paper highlights the potential for QRL utilization in coordinated mobility and satellite systems.",

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author = "Kim, \{Gyu Seon\} and Chen, \{Samuel Yen Chi\} and Soohyun Park and Joongheon Kim",

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AB - Reinforcement learning (RL) using conventional neural networks (NN) has significantly progressed in various applications. However, conventional RL needs help training in environments with large-scale action dimensions, such as coordinated mobility/satellite systems. Quantum reinforcement learning (QRL) with quantum NN (QNN) can address this problem through superposition and entanglement, one of the great features of quantum mechanics. Based on its 'i) fast convergence' and 'ii) high scalability', unique advantages of QRL that distinguish it from conventional RL, this paper highlights the potential for QRL utilization in coordinated mobility and satellite systems.

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M3 - Conference contribution

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BT - 2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 - Proceedings

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Quantum Reinforcement Learning for Coordinated Satellite Systems

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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