Hybrid Quantum-Inspired Optimization for AIGC-Driven IoT Task Offloading in MEC Networks

The rapid evolution of 6G networks and AI-generated content (AIGC) is reshaping service provisioning at the wireless edge, where low-latency and computation-intensive tasks must be efficiently supported. Integrating AIGC with 6G Internet of Things (IoT) ecosystems offers significant benefits, enabling real-time analytics, IoT data augmentation and synthesis, personalized services, and intelligent resource coordination across heterogeneous devices. However, provisioning AIGC services at the mobile edge poses formidable challenges: massive data heterogeneity, stringent delay requirements, and the inherent limitations of learning-based offloading methods, such as high training cost, unstable convergence, and weak transparency. Inspired by the quantum approximate optimization algorithm (QAOA), we propose a hybrid optimization framework that combines classical wireless bandwidth preallocation for IoT devices connected to mobile edge computing (MEC) servers with quadratic unconstrained binary optimization (QUBO)-based AIGC server selection. This hybrid classical-quantum design ensures stable results and enables efficient exploration of combinatorial allocation spaces. We further implement the quantum circuits and evaluate the performance of our proposed approach. Simulation results show that our method achieves lower processing latency and greater stability than conventional reinforcement learning and heuristic baselines in small- to medium-scale IoT deployments. While current quantum hardware scalability remains a constraint, the framework points toward a promising pathway for large-scale AIGC offloading as quantum technology matures.