  In this paper, a novel paradigm of mobile edge-quantum computing (MEQC) is
proposed, which brings quantum computing capacities to mobile edge networks
that are closer to mobile users (i.e., edge devices). First, we propose an MEQC
system model where mobile users can offload computational tasks to scalable
quantum computers via edge servers with cryogenic components and fault-tolerant
schemes. Second, we show that it is NP-hard to obtain a centralized solution to
the partial offloading problem in MEQC in terms of the optimal latency and
energy cost of classical and quantum computing. Third, we propose a multi-agent
hybrid discrete-continuous deep reinforcement learning using proximal policy
optimization to learn the long-term sustainable offloading strategy without
prior knowledge. Finally, experimental results demonstrate that the proposed
algorithm can reduce at least 30% of the cost compared with the existing
baseline solutions under different system settings.
