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.

翻译：量子联邦学习（QFL）结合了量子计算的鲁棒数据处理能力与联邦学习（FL）的隐私保护特性。然而，在大规模无线网络中，优化总速率对于释放QFL的真正潜力至关重要，它能在动态信道条件和波动的功率资源下，促进设备在有限带宽竞争中的有效模型共享与聚合。本文研究了一种新颖的总速率最大化问题，该问题置于基于非正交多址接入（NOMA）的大规模无线网络所设计的、多信道QFL框架内。我们通过联合考虑量子设备的信道选择与发射功率，构建了一个总速率最大化问题。我们构建的该问题是一个非凸、混合整数非线性规划（MINLP）难题，即使在给定信道选择参数的情况下，它仍然是非确定性多项式时间（NP）难问题。问题的复杂性促使我们创建一种有效的迭代优化方法，该方法利用精密的量子近似优化算法（QAOA）来推导高质量的近似解。此外，我们的研究首次对全设备参与下QFL的收敛特性进行了理论探索，严格分析了具有非凸损失函数、异构数据分布以及量子散粒噪声影响的现实场景。广泛的仿真结果表明，我们基于多信道NOMA的QFL框架改善了模型训练和收敛行为，在准确性和损失方面超越了传统算法。此外，我们以量子为中心的联合优化方法在确保快速收敛的同时，实现了总速率超过100%的提升，显著优于现有最先进方法。