量子联邦学习在无线网络中实现高效通信，提升模型训练速度与精度。
Communication-Efficient Quantum Federated Learning over Large-Scale Wireless Networks
- 采用基于非正交多址的多信道框架，联合优化设备选频与发射功率。
- 提出量子近似优化算法求解复杂问题，使总速率提升超100%。
- 首次分析全设备参与下的收敛性，适用于真实场景中的噪声与数据异构。
量子联邦学习（QFL）结合了量子计算的数据处理能力与联邦学习的隐私保护特性。在大规模无线网络中，优化总速率对充分发挥QFL潜力至关重要，因为设备需在动态信道条件和波动的功率资源下竞争有限带宽以实现有效模型共享与聚合。本文研究了一种新型多信道QFL框架下的总速率最大化问题，特别针对基于非正交多址接入（NOMA）的大规模无线网络。我们通过联合考虑量子设备的信道选择与发射功率，构建了一个非凸混合整数非线性规划（MINLP）问题，该问题即使在指定信道选择时仍为非确定性多项式时间（NP）难解。为此，我们设计了一种基于量子近似优化算法（QAOA）的迭代优化方法，获得高质量近似解。此外，本研究首次系统分析了全设备参与下QFL的收敛性，严格考察了非凸损失函数、异构数据分布及量子涨落噪声的影响。大量仿真表明，所提多信道NOMA-QFL框架显著改善了模型训练与收敛表现，其准确率与损失优于传统算法；且量子中心化联合优化方案使总速率提升超过100%，收敛迅速，明显优于现有方法。
原文摘要 · Abstract (English)
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.
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