Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

International Conference on Communications

Emergent Cooperation in Quantum Multi-Agent Reinforcement Learning Using Communication

Michael Kölle, Christian Reff, Leo Sünkel, Julian Hager, Gerhard Stenzel, Claudia Linnhoff–Popien

Published May 24, 2026• 0 citations
Publisher
DOI
10.1109/icc59461.2026.11587352

Related papers

Multi-agent Reinforcement Learning Using Simulated Quantum Annealing

Niels M. P. Neumann, Paolo B. U. L. de Heer, Irina Chiscop +1 more

Lecture notes in computer science•Jan 1, 2020•12 citations

Architectural Influence on Variational Quantum Circuits in Multi-Agent Reinforcement Learning: Evolutionary Strategies for Optimization

Michael Kölle, Karola Schneider, Sabrina Egger +5 more

arXiv:2407.20739•Lecture notes in computer science•Jan 1, 2025•1 citation

Emergent Cooperation in Quantum Multi-Agent Reinforcement Learning Using Communication

Michael Kölle, Christian Reff, Leo Sünkel +3 more

Emergent cooperation in classical Multi-Agent Reinforcement Learning has gained significant attention, particularly in the context of Sequential Social Dilemmas (SSDs). While classical reinforcement learning approaches have demonstrated capability for emergent cooperation, research on extending these methods to Quantum Multi-Agent Reinforcement Learning remains limited, particularly through communication. In this paper, we apply communication approaches to quantum Q-Learning agents: the Mutual Acknowledgment Token Exchange (MATE) protocol, its extension Mutually Endorsed Distributed Incentive Acknowledgment Token Exchange (MEDIATE), the peer rewarding mechanism Gifting, and Reinforced Inter-Agent Learning (RIAL). We evaluate these approaches in three SSDs: the Iterated Prisoner's Dilemma, Iterated Stag Hunt, and Iterated Game of Chicken. Our experimental results show that approaches using MATE with temporal-difference measure (MATE\textsubscript{TD}), AutoMATE, MEDIATE-I, and MEDIATE-S achieved high cooperation levels across all dilemmas, demonstrating that communication is a viable mechanism for fostering emergent cooperation in Quantum Multi-Agent Reinforcement Learning.

quant-pharXiv:2601.18419•Jan 27, 2026

Quantum multi-agent reinforcement learning for efficient resource allocation and trajectory optimization in multi-UAV IoT networks

R. Kiruthiga, B. Nithya

International Journal of Data Science and Analytics•Dec 5, 2025•2 citations

A Study of Multiagent Reinforcement Learning based on Quantum Theory

Meng Xiangping, Yuzhen Pi, Yuan Quande +1 more

The Multiconference on Computational Engineering in Systems Applications•Oct 1, 2006•1 citation

Quantum Multi-Agent Reinforcement Learning as an Emerging AI Technology: A Survey and Future Directions

Wenhan Yu, Jun Zhao

This paper surveys Quantum Multi-Agent Reinforcement Learning (QMARL), an emerging fusion of quantum computing and multi-agent systems. It begins by introducing the transformative potential of quantum computing in computational capabilities. Examining the principles of multi-agent reinforcement learning (MARL), the paper explores how quantum computing enhances learning efficiency and decision-making. Focusing on the current state of QMARL, it reviews literature, methodologies, and case studies showcasing the integration of quantum algorithms with MARL frameworks. The survey addresses challenges and opportunities arising from quantum technologies in multi-agent systems, such as entanglement and superposition, exploring their implications for agent coordination and learning dynamics. Practical applications in domains like cybersecurity and finance underscore QMARL’s transformative potential. Concluding, the paper identifies research gaps and proposes future directions, emphasizing the need for scalable quantum algorithms, exploring quantum-resistant strategies in adversarial settings, and integrating quantum principles in agent communication and collaboration. This concise survey serves as a foundational guide for researchers and practitioners, offering insights into the current state and future possibilities of QMARL.

International Conference on Computer and Applications (ICCA)•Nov 28, 2023•6 citations
Emergent cooperation in classical Multi-Agent Reinforcement Learning has gained significant attention, particularly in the context of Sequential Social Dilemmas (SSDs). While classical reinforcement learning approaches have demonstrated capability for emergent cooperation, research on extending these methods to Quantum Multi-Agent Reinforcement Learning remains limited, particularly through communication. In this paper, we apply communication approaches to quantum Q-Learning agents: the Mutual Acknowledgment Token Exchange (MATE) protocol, its extension Mutually Endorsed Distributed Incentive Acknowledgment Token Exchange (MEDIATE), the peer rewarding mechanism Gifting, and Reinforced Inter-Agent Learning (RIAL). We evaluate these approaches in three SSDs: the Iterated Prisoner's Dilemma, Iterated Stag Hunt, and Iterated Game of Chicken. Our experimental results show that approaches using MATE with temporal-difference measure (MATE\textsubscript{TD}), AutoMATE, MEDIATE-I, and MEDIATE-S achieved high cooperation levels across all dilemmas, demonstrating that communication is a viable mechanism for fostering emergent cooperation in Quantum Multi-Agent Reinforcement Learning.
This paper surveys Quantum Multi-Agent Reinforcement Learning (QMARL), an emerging fusion of quantum computing and multi-agent systems. It begins by introducing the transformative potential of quantum computing in computational capabilities. Examining the principles of multi-agent reinforcement learning (MARL), the paper explores how quantum computing enhances learning efficiency and decision-making. Focusing on the current state of QMARL, it reviews literature, methodologies, and case studies showcasing the integration of quantum algorithms with MARL frameworks. The survey addresses challenges and opportunities arising from quantum technologies in multi-agent systems, such as entanglement and superposition, exploring their implications for agent coordination and learning dynamics. Practical applications in domains like cybersecurity and finance underscore QMARL’s transformative potential. Concluding, the paper identifies research gaps and proposes future directions, emphasizing the need for scalable quantum algorithms, exploring quantum-resistant strategies in adversarial settings, and integrating quantum principles in agent communication and collaboration. This concise survey serves as a foundational guide for researchers and practitioners, offering insights into the current state and future possibilities of QMARL.