Recent advancements in quantum computing, alongside successful deployments of quantum communication, hold promises for revolutionizing mobile networks. While Quantum Machine Learning (QML) presents opportunities, it contends with challenges like noise in quantum devices and scalability. Furthermore, the high cost of quantum communication constrains the practical application of QML in real-world scenarios. This paper introduces a noise-aware clustered quantum federated learning system that addresses noise mitigation, limited quantum device capacity, and high quantum communication costs in distributed QML. It employs noise modelling and clustering to select devices with minimal noise and distribute QML tasks efficiently. Using circuit partitioning to deploy smaller models on low-noise devices and aggregating similar devices, the system enhances distributed QML performance and reduces communication costs. Leveraging circuit cutting, QML techniques are more effective for smaller circuit sizes and fidelity. We conduct experimental evaluations to assess the performance of the proposed system. Additionally, we introduce a noisy dataset for QML to demonstrate the impact of noise on proposed accuracy.

翻译：近期量子计算的进展，结合量子通信的成功部署，为移动网络的变革带来了希望。虽然量子机器学习（QML）展现出机遇，但也面临着量子设备噪声和可扩展性等挑战。此外，量子通信的高成本限制了QML在实际场景中的应用。本文提出了一种噪声感知的集群量子联邦学习系统，旨在解决分布式QML中的噪声缓解、量子设备容量有限以及量子通信成本高昂的问题。该系统利用噪声建模和聚类技术，选择噪声最小的设备并高效分配QML任务。通过电路分区在低噪声设备上部署较小模型，并聚合相似设备，该系统提升了分布式QML的性能并降低了通信成本。借助电路切割技术，QML方法在较小电路规模和保真度下更为有效。我们进行了实验评估以检验所提系统的性能。此外，我们引入了一个用于QML的噪声数据集，以展示噪声对所提方法准确性的影响。