In modern network applications, the concept of metaverse is an emerging technology, and one of its key characteristics is the separation between the physical-space of the users and the virtual meta-space of the avatars. Based on this, a novel quantum multi-agent reinforcement learning (QMARL)-based scheduler, prioritizing the improvement of the performance of meta-space rendering is proposed in this article. The increase in performance can be achieved by controlling the number of utilized point cloud segments depending on avatar-popularity under the consideration of physical-space constraints. During this process, QMARL-centered quantum artificial intelligence (AI) algorithms are utilized for sequential scheduling action decision-making over time. By utilizing this QMARL, our proposed scheduler is designed to reduce the number of scheduling action dimensions into logarithmic scales. This is beneficial in the noisy intermediate-scale quantum (NISQ) era as the number of usable qubits is inherently limited. After this QMARL-based scheduling, the scheduled regions will be re-constructed over meta-space with prioritized point cloud registration. Finally, our performance evaluation results show that our proposed scheduler outperforms classical multi-agent reinforcement learning (MARL)-based scheduling in terms of utility between 12.6 and 20.7 percent, under various avatar distributions.
In modern network applications, the concept of metaverse is an emerging technology, and one of its key characteristics is the separation between the physical-space of the users and the virtual meta-space of the avatars. Based on this, a novel quantum multi-agent reinforcement learning (QMARL)-based scheduler, prioritizing the improvement of the performance of meta-space rendering is proposed in this article. The increase in performance can be achieved by controlling the number of utilized point cloud segments depending on avatar-popularity under the consideration of physical-space constraints. During this process, QMARL-centered quantum artificial intelligence (AI) algorithms are utilized for sequential scheduling action decision-making over time. By utilizing this QMARL, our proposed scheduler is designed to reduce the number of scheduling action dimensions into logarithmic scales. This is beneficial in the noisy intermediate-scale quantum (NISQ) era as the number of usable qubits is inherently limited. After this QMARL-based scheduling, the scheduled regions will be re-constructed over meta-space with prioritized point cloud registration. Finally, our performance evaluation results show that our proposed scheduler outperforms classical multi-agent reinforcement learning (MARL)-based scheduling in terms of utility between 12.6 and 20.7 percent, under various avatar distributions.
A metaverse is composed of a physical-space and virtual-space, with the aim of having users in both the virtual reality and the real world experience. Prioritization is essential, but it is not straight-forwarded due to the limitation of computing resources in real-world, making it impossible to synchronize all data. Therefore, it is crucial to allocate resources based on regional preferences in physical-space and content preferences of users in virtual-space. The referencing system consists of two-stage sub-computations, (i) spatial-prioritization for more data gathering under the consideration of avatar-popularity one top of physical-space and (ii) temporal-prioritization for virtual-space rendering under the avatar-popularity. Both prioritization tasks are combinatorics problems and are well-known NP-hard. The problem scale is also large, making it difficult to solve within given times. The classical deep learning cannot be the solution. Quantum-based learning algorithms can be the potential solutions due to high-performance computing capabilities, because a small number of qubits can represent an exponentially large amount of information. On top of these advantages, an improved quantum reinforcement learning (QRL) algorithm is proposed for reducing the control dimensions into a logarithmic scale. We corroborate that our proposed QRL-based algorithm for low-dimensional spatio-temporal prioritization improves convergence and performance.
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.
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.
Although quantum supremacy is yet to come, there has recently been an increasing interest in identifying the potential of quantum machine learning (QML) in the looming era of practical quantum computing. Motivated by this, in this article we re-design multi-agent reinforcement learning (MARL) based on the unique characteristics of quantum neural networks (QNNs) having two separate dimensions of trainable parameters: angle parameters affecting the output qubit states, and pole parameters associated with the output measurement basis. Exploiting this dyadic trainability as meta-learning capability, we propose quantum meta MARL (QM2ARL) that first applies angle training for meta-QNN learning, followed by pole training for few-shot or local-QNN training. To avoid overfitting, we develop an angle-to-pole regularization technique injecting noise into the pole domain during angle training. Furthermore, by exploiting the pole as the memory address of each trained QNN, we introduce the concept of pole memory allowing one to save and load trained QNNs using only two-parameter pole values. We theoretically prove the convergence of angle training under the angle-to-pole regularization, and by simulation corroborate the effectiveness of QM2ARL in achieving high reward and fast convergence, as well as of the pole memory in fast adaptation to a time-varying environment.