cs.ITMar 1, 2026

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

Authors: Shaba Shaon, Christopher G. Brinton, Dinh C. Nguyen

Organizations: ECE Department, The University of Alabama in Huntsville, Huntsville, AL 35899, USA · Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA

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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 sophisticated quantum approximate optimization algorithm (QAOA) to derive high-quality approximate solutions. Additionally, our study presents the first theoretical exploration of QFL convergence properties under full device participation, rigorously analyzing real-world scenarios with nonconvex loss functions, diverse data distributions, and the effects of quantum shot noise. Extensive simulation results indicate that our multi-channel NOMA-based QFL framework enhances model training and convergence behavior, surpassing conventional algorithms in terms of accuracy and loss. Moreover, our quantum-centric joint optimization approach achieves more than a 100% increase in sum-rate while ensuring rapid convergence, significantly outperforming the state-of-the-arts.

Figures & tables

Fig. 1 : Proposed QFL framework where distributed quantum devices collaborate with a central server to train a shared ML model. Each device encodes practical data into quantum form using a state encoder, processes it through a PQC with trainable angle parameters, and then employs measurement outcomes to update local model parameters using the standard SGD method, subsequently sending the updated local models to the server for aggregation.
Fig. 2 : Training performance comparison of QFL for MNIST dataset (IID) for full device participation with varying number of quantum measurement shots.
Fig. 3 : Training performance comparison of QFL on Cifar10 and MNIST datasets (non-IID) for full device participation with varying number of quantum measurement shots.
Fig. 4 : Comparison of transmit power and channel selection optimization using QAOA and SCA for varying device numbers.
Fig. 5 : Comparative analysis of sum-rate performance across optimization schemes for varying number of devices.
Fig. 6 : Comparison of sum-rate performance across different schemes as devices maximum transmit power increases for varying number of devices.
Fig. 7 : Comparison of latency performance across different schemes as device’s maximum transmit power increases for varying number of devices.
Fig. 8 : Comparative analysis of latency performance across optimization schemes for varying number of devices.
Number of devicesQAOA ExecutionSCA Execution
 Time (s)Time (s)
50187.332245.08
200730.7536562.43
TABLE I : Execution Times of QAOA and SCA
Number of devicesSub-problemSub-problemJoint
 1 (s)2 (s)(s)
5046.74140.33187.33
200188.53540.13730.75
500470.791365.881839.88
TABLE II : Execution Times for Different QAOA Optimization Strategies and device Numbers
Shaba Shaon is currently pursuing her Ph.D. in the Department of Electrical and Computer Engineering at The University of Alabama in Huntsville, USA. Her research interests focus on distributed quantum computing, federated learning, wireless networking.
Table 11
Christopher G. Brinton [Senior Member, IEEE] is currently the Elmore Associate Professor of Electrical and Computer Engineering with Purdue University, USA. He was a recipient of the four U.S. Top Early Career Awards from the National Science Foundation (CAREER), the Office of Naval Research (YIP), the Defense Advanced Research Projects Agency (YFA), and the Air Force Office of Scientific Research (YIP).
Table 12
Dinh C. Nguyen is an assistant professor at the Department of Electrical and Computer Engineering, The University of Alabama in Huntsville, USA. His research interests focus on distributed quantum computing, federated learning, wireless networking.
Table 13
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