针对量子噪声差异，提出间歇式联邦学习框架提升训练稳定性
Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise
- 根据噪声波动动态调整训练策略，缓解量子设备噪声差异
- 实测显示模型收敛更稳定，性能显著优于传统方案
- 适合在噪声不均的分布式量子系统中部署应用
量子联邦学习（QFL）结合量子计算与联邦学习，实现量子网络上的去中心化模型训练并保护数据隐私。然而，量子噪声仍是主要障碍，因现代量子设备受硬件质量差异和量子退相干影响，存在异构噪声水平，导致训练效果不佳。为此，本文提出SpoQFL框架，利用间歇式学习机制应对分布式量子系统中的量子噪声异构问题。该框架根据噪声波动动态调整训练策略，增强模型鲁棒性、收敛稳定性与整体学习效率。在真实数据集上的大量实验表明，SpoQFL显著优于传统QFL方法，实现了更优的训练性能与更稳定的收敛表现。
原文摘要 · Abstract (English)
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.
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