标题： 量子环境中的偶然联邦学习方法以应对量子噪声 显示英文标题

标题： Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise

摘要： 量子联邦学习（QFL）是一种新兴的范式，将量子计算和联邦学习（FL）相结合，以在量子网络上实现去中心化的模型训练，同时保持数据隐私。 然而，量子噪声仍然是QFL中的一个重要障碍，因为现代量子设备由于硬件质量的差异和对量子退相干的敏感性，会经历异构的噪声水平，导致训练性能不足。 为了解决这个问题，我们提出了SpoQFL，这是一种新颖的QFL框架，利用间歇性学习来缓解分布式量子系统中的量子噪声异构性。 SpoQFL根据噪声波动动态调整训练策略，提高模型的鲁棒性、收敛稳定性和整体学习效率。 在真实数据集上的大量实验表明，SpoQFL显著优于传统的QFL方法，实现了更优越的训练性能和更稳定的收敛。 显示英文摘要

摘要： Quantum Federated Learning (QFL) is an emerging paradigm that combines quantum computing and federated learning (FL) to enable decentralized model training while maintaining data privacy over quantum networks. However, quantum noise remains a significant barrier in QFL, since modern quantum devices experience heterogeneous noise levels due to variances in hardware quality and sensitivity to quantum decoherence, resulting in inadequate training performance. To address this issue, we propose SpoQFL, a novel QFL framework that leverages sporadic learning to mitigate quantum noise heterogeneity in distributed quantum systems. SpoQFL dynamically adjusts training strategies based on noise fluctuations, enhancing model robustness, convergence stability, and overall learning efficiency. Extensive experiments on real-world datasets demonstrate that SpoQFL significantly outperforms conventional QFL approaches, achieving superior training performance and more stable convergence.