Cost-Effective Task Offloading Scheduling for Hybrid Mobile Edge-Quantum Computing

In this paper, we aim to address the challenge of hybrid mobile edge-quantum computing (MEQC) for sustainable task offloading scheduling in mobile networks. We develop cost-effective designs for both task offloading mode selection and resource allocation, subject to the individual link latency constraint guarantees for mobile devices, while satisfying the required success ratio for their computation tasks. Specifically, this is a time-coupled offloading scheduling optimization problem in need of a computationally affordable and effective solution. To this end, we propose a deep reinforcement learning (DRL)-based Lyapunov approach. More precisely, we reformulate the original time-coupled challenge into a…

• NS
  Natural Science Foundation of Sichuan Province

  Award: 2026NSFSC0389
• NK
  National Key Research and Development Program of China

  Award: 2025YFE0100900