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

Soohyun Park, Hankyul Baek, Joongheon Kim

Abstract: 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.