Abstract
Exploiting quantum computing at the mobile edge holds immense potential for
facilitating large-scale network design, processing multimodal data, optimizing
resource management, and enhancing network security. In this paper, we propose
a pioneering paradigm of mobile edge quantum computing (MEQC) that integrates
quantum computing capabilities into classical edge computing servers that are
proximate to mobile devices. To conceptualize the MEQC, we first design an MEQC
system, where mobile devices can offload classical and quantum computation
tasks to edge servers equipped with classical and quantum computers. We then
formulate the hybrid classical-quantum computation offloading problem whose
goal is to minimize system cost in terms of latency and energy consumption. To
solve the offloading problem efficiently, we propose a hybrid
discrete-continuous multi-agent reinforcement learning algorithm to learn
long-term sustainable offloading and partitioning strategies. Finally,
numerical results demonstrate that the proposed algorithm can reduce the MEQC
system cost by up to 30% compared to existing baselines.