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