Communication-Efficient Quantum Federated Learning over Large-Scale Wireless Networks

Researchers present a novel quantum federated learning framework for large-scale wireless networks that combines quantum computing with privacy-preserving federated learning. The study introduces a sum-rate maximization approach using quantum approximate optimization algorithm (QAOA) that achieves over 100% improvement in performance compared to conventional methods.

• →Quantum federated learning (QFL) combines quantum computing's processing power with federated learning's privacy benefits for wireless networks.
• →The research addresses sum-rate optimization in multi-channel NOMA-based networks using quantum approximate optimization algorithm (QAOA).
• →The proposed framework achieves more than 100% increase in sum-rate while ensuring rapid convergence compared to existing algorithms.
• →This represents the first theoretical exploration of QFL convergence properties under full device participation with real-world constraints.
• →The solution tackles the NP-hard mixed-integer nonlinear programming challenge through iterative quantum optimization.