The Matrix: Quantum AI for Interacting Two Worlds in Prioritized Metaverse Spaces

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.