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.
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.
This paper presents a comprehensive survey of Quantum Multi-Agent Reinforcement Learning (QMARL), a nascent field at the intersection of quantum computing and multi-agent systems. The survey begins by introducing the fundamentals of quantum computing, highlighting its potential to revolutionize computational capabilities. We then delve into the principles of multi-agent reinforcement learning (MARL), examining how quantum computing can enhance learning efficiency and decision-making processes in complex environments. The core of the survey focuses on the current state of QMARL, reviewing existing literature, methodologies, and case studies that demonstrate the integration of quantum algorithms with MARL frameworks. The paper also addresses the unique challenges and opportunities presented by quantum technologies in multi-agent systems, such as quantum entanglement and superposition, and their implications for agent coordination and learning dynamics. Additionally, the survey explores the practical applications of QMARL in various domains, including cybersecurity, finance, and robotics, underscoring its transformative potential. The paper concludes by identifying key research gaps and proposing future directions for the development of QMARL. This includes the need for scalable quantum algorithms, the exploration of quantum-resistant strategies in adversarial settings, and the integration of quantum principles in agent communication and collaboration. Overall, this survey serves as a foundational guide for researchers and practitioners interested in the emerging field of QMARL, offering insights into its current achievements and future possibilities.
This paper presents a comprehensive survey of Quantum Multi-Agent Reinforcement Learning (QMARL), a nascent field at the intersection of quantum computing and multi-agent systems. The survey begins by introducing the fundamentals of quantum computing, highlighting its potential to revolutionize computational capabilities. We then delve into the principles of multi-agent reinforcement learning (MARL), examining how quantum computing can enhance learning efficiency and decision-making processes in complex environments. The core of the survey focuses on the current state of QMARL, reviewing existing literature, methodologies, and case studies that demonstrate the integration of quantum algorithms with MARL frameworks. The paper also addresses the unique challenges and opportunities presented by quantum technologies in multi-agent systems, such as quantum entanglement and superposition, and their implications for agent coordination and learning dynamics. Additionally, the survey explores the practical applications of QMARL in various domains, including cybersecurity, finance, and robotics, underscoring its transformative potential. The paper concludes by identifying key research gaps and proposing future directions for the development of QMARL. This includes the need for scalable quantum algorithms, the exploration of quantum-resistant strategies in adversarial settings, and the integration of quantum principles in agent communication and collaboration. Overall, this survey serves as a foundational guide for researchers and practitioners interested in the emerging field of QMARL, offering insights into its current achievements and future possibilities.
Advancements in quantum computer hardware and software have paved the way for the application of quantum computing across various fields. With the development of computers with thousands of qubits, the power of quantum is increasing. Because of efficiency in high action dimensions, quantum computing has advantages in multi-agent fields where the amount of data is huge. At the same time, reinforcement learning (RL) has gained prominence for its potential in unknown environments. Consequently, the integration of quantum computing and multi-agent reinforcement learning (MARL), known as quantum multi-agent reinforcement learning (QMARL), is proposed. This paper introduces the overall concept and applications of quantum computing, MARL, and QMARL. It also shows the current technology trends depending on the application. In conclusion, the paper presents challenges and future research directions for QMARL and shows that QMARL can be utilized in various applications for excellent performance.
Many challenges arising in Quantum Technology can be successfully addressed using a set of machine learning algorithms collectively known as reinforcement learning (RL), based on adaptive decision-making through interaction with the quantum device. After a concise and intuitive introduction to RL aimed at a broad physics readership, we discuss the key ideas and core concepts in reinforcement learning with a particular focus on quantum systems. We then survey recent progress in RL in all relevant areas. We discuss state preparation in few- and many-body quantum systems, the design and optimization of high-fidelity quantum gates, and the automated construction of quantum circuits, including applications to variational quantum eigensolvers and architecture search. We further highlight the interactive capabilities of RL agents, emphasizing recent progress in quantum feedback control and quantum error correction, and briefly discuss quantum reinforcement learning as well as applications to quantum metrology. The review concludes with a discussion of open challenges -- such as scalability, interpretability, and integration with experimental platforms -- and outlines promising directions for future research. Throughout, we highlight experimental implementations that exemplify the increasing role of reinforcement learning in shaping the development of quantum technologies.
In the last decade, the field of reinforcement learning has evolved from single-agent paradigms to embrace multi-agent settings. However, as the number of agents increases, especially in intricate or stochastic environments, the efficacy of individual learning models tends to diminish. Moreover, applying experience replay techniques in multi-agent scenarios presents considerable challenges. To address these pressing issues, this paper introduces a straightforward yet highly effective approach known as Quantum-Based Multi-Agent Reinforcement Learning (QMARL). This approach revolves around the quantization of states and actions within the multi-agent reinforcement learning system. Leveraging the power of the Grover algorithm for action decision-making, we also introduce a novel quantum-based prioritized experience replay method. Our proposed approach has been rigorously validated through experiments conducted in the cooperative navigation environment provided by OpenAI. The results demonstrate its capacity to enhance multi-agent learning in complex settings. This research opens promising avenues for harnessing quantum computing techniques in the realm of reinforcement learning, paving the way for more robust and scalable solutions in multi-agent systems.
Collaboration is a key challenge in distributed multi-agent reinforcement learning (MARL) environments. Learning frameworks for these decentralized systems must weigh the benefits of explicit player coordination against the communication overhead and computational cost of sharing local observations and environmental data. Quantum computing has sparked a potential synergy between quantum entanglement and cooperation in multi-agent environments, which could enable more efficient distributed collaboration with minimal information sharing. This relationship is largely unexplored, however, as current state-of-the-art quantum MARL (QMARL) implementations rely on classical information sharing rather than entanglement over a quantum channel as a coordination medium. In contrast, in this paper, a novel framework dubbed entangled QMARL (eQMARL) is proposed. The proposed eQMARL is a distributed actor-critic framework that facilitates cooperation over a quantum channel and eliminates local observation sharing via a quantum entangled split critic. Introducing a quantum critic uniquely spread across the agents allows coupling of local observation encoders through entangled input qubits over a quantum channel, which requires no explicit sharing of local observations and reduces classical communication overhead. Further, agent policies are tuned through joint observation-value function estimation via joint quantum measurements, thereby reducing the centralized computational burden. Experimental results show that eQMARL with Ψ+ entanglement converges to a cooperative strategy up to 17.8% faster and with a higher overall score compared to split classical and fully centralized classical and quantum baselines. The results also show that eQMARL achieves this performance with a constant factor of 25-times fewer centralized parameters compared to the split classical baseline.