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

arXiv: https://arxiv.org/abs/2603.01222 · HTML: https://mlsanity.com/paper/2603.01222/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 45 references that are in the map.
- First posted on arXiv 1 Mar 2026.

## Builds on (most cited first, 12 of 12)

- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [On the Convergence of SGD with Biased Gradients](https://mlsanity.com/paper/2008.00051/) (2020)
- [Foundations of Quantum Federated Learning Over Classical and Quantum Networks](https://mlsanity.com/paper/2310.14516/) (2023)
- [Federated Quantum Machine Learning with Differential Privacy](https://mlsanity.com/paper/2310.06973/) (2023)
- [Quantum Federated Learning With Quantum Networks](https://mlsanity.com/paper/2310.15084/) (2023)
- [A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret](https://mlsanity.com/paper/2301.08869/) (2023)
- [UAV Relay-Assisted Emergency Communications in IoT Networks: Resource Allocation and Trajectory Optimization](https://mlsanity.com/paper/2008.00218/) (2020)
- [Error Mitigation-Aided Optimization of Parameterized Quantum Circuits: Convergence Analysis](https://mlsanity.com/paper/2209.11514/) (2022)
- [OFDMA-F2L: Federated Learning With Flexible Aggregation Over an OFDMA Air Interface](https://mlsanity.com/paper/2311.15141/) (2023)
- [From Federated Learning to Quantum Federated Learning for Space-Air-Ground Integrated Networks](https://mlsanity.com/paper/2411.01312/) (2024)
- [A Learning Approach for Low-Complexity Optimization of Energy Efficiency in Multi-Carrier Wireless Networks](https://mlsanity.com/paper/1802.09591/) (2018)

## Abstract (arXiv)

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.