发表机构
Korea University; Wells Fargo（高丽大学; 富国银行）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对量子联邦学习中设备差异导致共享架构不适配及参数平均语义不一致的问题，提出原型引导的个性化量子神经架构搜索方法，通过类级原型共享实现协作，提升准确率3.70%。
AI 中文摘要
量子联邦学习（QFL）已成为一种有前景的方法，用于在资源受限设备上的分布式私有数据上协作训练紧凑的量子神经网络（QNN）。然而，设备能力的差异使得单一共享的QNN架构不适合所有客户端。虽然个性化量子神经架构搜索（QNAS）允许每个客户端选择特定于设备的QNN，但跨结构不同的QNN架构对参数进行平均会混合语义上不一致的电路操作。为了解决这个问题，提出了面向虚拟联邦学习（vFedProtoQNAS）的原型引导个性化QNAS，其中模型参数从不跨客户端聚合，联邦协作通过类级原型共享实现。每个客户端独立搜索并训练特定于客户端的QNN，从潜在表示中计算类级局部原型，并使用来自服务器的全局原型作为联邦语义锚点对其进行细化。实验表明，vFedProtoQNAS相比FedAvg将准确率提高了3.70%，并增强了类一致表示对齐。
英文摘要
Quantum federated learning (QFL) has emerged as a promising approach for collaboratively training compact quantum neural networks (QNNs) over distributed private data on resource-constrained devices. However, differences in device capabilities make a single shared QNN architecture unsuitable for all clients. While personalized quantum neural architecture search (QNAS) allows each client to select a device-specific QNN, averaging parameters across structurally different QNN architectures mixes semantically inconsistent circuit operations. To address this, prototype-guided personalized QNAS for virtual FL (vFedProtoQNAS) is proposed, where model parameters are never aggregated across clients and federated collaboration is achieved through class-wise prototype sharing. Each client independently searches and trains a client-specific QNN, computes class-wise local prototypes from latent representations, and refines them using global prototypes from the server as federated semantic anchors. Experiments demonstrate that vFedProtoQNAS improves accuracy by 3.70\% over FedAvg and enhances class-consistent representation alignment.
