解决量子联邦学习中硬件噪声与数据异构的双重干扰问题
Q-ANCHOR: Federated Quantum Learning with ZNE-guided Correction
- 用零噪声外推锚定服务器更新，结合客户端状态修正
- 实验显示训练更稳定，误差下限显著降低
- 适合量子机器学习、分布式量子计算研究者
量子联邦学习（QFL）为在保持数据本地性的前提下跨分布式客户端训练量子模型提供了前景广阔的框架。由于简单且通信开销低，联邦平均（FedAvg）是当前QFL领域的标准聚合方法。然而，在实际硬件上部署时，会暴露严重的双重漂移现象：全局模型同时受非独立同分布（non-IID）数据导致的客户端漂移和噪声量子梯度估计带来的硬件偏差影响。本文首次分析了在真实条件下FedAvg的收敛性，数学证明了硬件偏差会引入持续存在的误差下限，常规平均无法消除。为此，我们提出Q-ANCHOR，一种量子感知的联邦聚合架构，通过零噪声外推（ZNE）锚定服务器更新，并应用有状态客户端校正以抑制客户端漂移与硬件偏差。收敛性理论证明，Q-ANCHOR能有效缓解经典客户端漂移并主动降低硬件偏差下限。实验结果表明，相比传统联邦学习基线，Q-ANCHOR实现显著更稳定的训练。
原文摘要 · Abstract (English)
Quantum Federated Learning (QFL) offers a promising framework to train quantum models across distributed clients while keeping data strictly local. Due to its simplicity and low communication overhead, Federated Averaging (FedAvg) is the standard aggregation choice in QFL literature. However, deploying QFL on practical hardware exposes a severe double-drift phenomenon: the global model is simultaneously derailed by client drift from non-IID data and hardware bias from noisy quantum gradient estimates. In this work, we first analyze the convergence of FedAvg under these realistic conditions, mathematically demonstrating that quantum hardware bias creates a persistent error floor that standard averaging cannot correct. To overcome this limitation, we propose Q-ANCHOR, a quantum-aware federated aggregation architecture that anchors server updates with zero-noise extrapolation while applying stateful client correction to suppress both client drift and hardware-induced bias. Our convergence theory proves that Q-ANCHOR successfully mitigates classical client drift while actively reducing the hardware-bias floor. Experimental results demonstrate that Q-ANCHOR achieves significantly more stable training than conventional FL baselines.
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