Quantum Reinforcement Learning for Spatio-Temporal Prioritization in Metaverse

Abstract

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.