用量子方法提升车载联邦学习的抗噪能力，更安全高效。
Noise-Resilient Quantum Aggregation on NISQ for Federated ADAS Learning
- 用变分量子电路在嘈杂设备上聚合模型参数
- 实测收敛稳定，梯度方差降低，通信开销更小
- 适合车联网中对安全与实时性要求高的场景
高级驾驶辅助系统（ADAS）越来越多地采用联邦学习（FL）在分布式车载节点间协作训练模型，同时保护数据隐私。然而，传统联邦学习在实时车载网络中易受噪声、延迟和安全限制影响。本文提出噪声鲁棒量子联邦学习（NR-QFL），一种混合量子-经典框架，通过在嘈杂中等规模量子（NISQ）条件下运行的变分量子电路（VQC），实现安全、低延迟的聚合。该框架将模型参数编码为量子态，并采用自适应门重参数化，确保有界误差收敛，且在完全正定保迹（CPTP）动力学下具备可证明的鲁棒性。NR-QFL使用基于量子熵的客户端选择与多服务器协同机制，保障公平性与稳定性。实验验证显示，在受限边缘条件下，该框架具备一致收敛性，梯度方差更低，通信开销更小，抗噪能力更强。该框架为量子增强的联邦学习建立了可扩展基础，支持车联网边缘端的安全、高效、动态稳定的智能决策。
原文摘要 · Abstract (English)
Advanced Driver Assistance Systems (ADAS) increasingly employ Federated Learning (FL) to collaboratively train models across distributed vehicular nodes while preserving data privacy. Yet, conventional FL aggregation remains susceptible to noise, latency, and security constraints inherent to real-time vehicular networks. This paper introduces Noise-Resilient Quantum Federated Learning (NR-QFL), a hybrid quantum-classical framework that enables secure, low-latency aggregation through variational quantum circuits (VQCs) operating under Noisy Intermediate-Scale Quantum (NISQ) conditions. The framework encodes model parameters as quantum states with adaptive gate reparameterization, ensuring bounded-error convergence and provable resilience under Completely Positive Trace-Preserving (CPTP) dynamics. NR-QFL employs quantum entropy-based client selection and multi-server coordination for fairness and stability. Empirical validation shows consistent convergence with reduced gradient variance, lower communication overhead, and enhanced noise tolerance under constrained edge conditions. The framework establishes a scalable foundation for quantum-enhanced federated learning, enabling secure, efficient, and dynamically stable ADAS intelligence at the vehicular edge.
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