Warm and Cold Start Quantum Annealing for Metaverse Resource Optimization

Abstract

Metaverse refers to the intersection of parallel virtual worlds with their physical counterparts by allowing users to interact with virtual people, objects, and environments. Resource allocation in various aspects of Metaverse domains, called as MetaSlices hereinafter, is a crucial optimization research problem. To serve this purpose, we consider a MetaSlice framework with the notion of sharing resources among common functions and enable placing time-sensitive services at the edge of multi-tier architecture in proximity to users. Unfortunately, the classical Integer Linear Programming is inappropriate for such heavily constrained optimization problem due to the extensive running time and memory. Hence, we model a novel Quadratic Unconstrained Binary Optimization (QUBO) formulation to simultaneously optimize resources and secure Quality of Service for MetaSlices as a paradigm shift towards quantum computing. Furthermore, we propose to employ two hybrid classical-quantum strategies, Warm Start and Cold Start Quantum Annealing to optimize resource under bandwidth uncertainty, offer ultra-low running time, and increase service acceptance rate/scalability in a resource-hungry and dynamic Metaverse system.

Keywords

• Quantum annealing
• metaverse
• combinatorial optimization
• warm start
• cold start
• stochastic optimization