Hybrid Quantum-Classical Computing for Deterministic & Stochastic Combinatorial Optimization in the Internet of Everything

Abstract

Internet of Everything (IoE) refers to the intelligent connection among people, things, data and processes, essentially inclining toward the next-generation networks. To unleash the full potential of the IoE framework, it often requires nearly real-time management decisions regarding deployment and scheduling operations. The IoE service management problems, primarily formulated as traditional mathematical programming models, are NP-hard, hence inappropriate for time-sensitive IoE environments. This thesis promotes the need to go beyond the realms and leverage quantum computing-based service management. Next, the feasibility of applying quantum computing for IoE optimization, based on today's available quantum resources (qubits), is intended for evaluation. Further along this line, the vision includes determining the advantages of solving NP-hard problems using quantum computing over classical approaches, as well as identifying major trade-offs that hinder applicability. Based on the research progress, hybrid quantum-classical computing is the key to solve futuristic IoE service management optimizations, considering the limited availability of quantum computing resources (qubits). The synergistic research for quantum computing-enabled network optimization and analytics can be regarded as an effort to scale down massive resource fabrication costs and upgrade profit margins for service providers.