[2603.01222] Communication-Efficient Quantum Federated Learning over Large-Scale Wireless Networks

Computer Science > Information Theory arXiv:2603.01222 (cs) [Submitted on 1 Mar 2026] Title:Communication-Efficient Quantum Federated Learning over Large-Scale Wireless Networks Authors:Shaba Shaon, Christopher G. Brinton, Dinh C. Nguyen View a PDF of the paper titled Communication-Efficient Quantum Federated Learning over Large-Scale Wireless Networks, by Shaba Shaon and 2 other authors View PDF HTML (experimental) Abstract:Quantum federated learning (QFL) combines the robust data processing of quantum computing with the privacy-preserving features of federated learning (FL). However, in large-scale wireless networks, optimizing sum-rate is crucial for unlocking the true potential of QFL, facilitating effective model sharing and aggregation as devices compete for limited bandwidth amid dynamic channel conditions and fluctuating power resources. This paper studies a novel sum-rate maximization problem within a muti-channel QFL framework, specifically designed for non-orthogonal multiple access (NOMA)-based large-scale wireless networks. We develop a sum-rate maximization problem by jointly considering quantum device's channel selection and transmit power. Our formulated problem is a non-convex, mixed-integer nonlinear programming (MINLP) challenge that remains non-deterministic polynomial time (NP)-hard even with specified channel selection parameters. The complexity of the problem motivates us to create an effective iterative optimization approach that utilizes the sophis...