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English(EN) A Stable Aggregation Method for Quantum Federated Learning

量子联邦学习研究应对噪声和异构性

两篇新研究论文探讨了量子联邦学习(QFL)的进展,这是一种允许量子神经网络在不共享私有数据的情况下进行训练的方法。第一篇论文介绍了一种使用 QoS 感知客户端加权和圆形参数聚合的稳定聚合方法,该方法在 IBM 量子机器上进行了验证,并在医疗和金融数据集上显示出更高的稳定性和准确性。第二篇论文提出使用 Bures--Uhlmann 几何进行 QFL,将参数空间几何扩展到混合态,以处理嘈杂和异构的量子设备,在囚禁离子量子模拟器上展示了优于标准联邦平均的准确性。 AI

影响 量子联邦学习的这些进展可能能够实现跨分布式、嘈杂的量子硬件对量子模型进行更鲁棒和准确的训练。

排序理由 两篇在 arXiv 上发表的学术论文,详细介绍了量子联邦学习的新方法。

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量子联邦学习研究应对噪声和异构性

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两篇在 arXiv 上发表的学术论文,详细介绍了量子联邦学习的新方法。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Shanika Nanayakkara, Shiva Raj Pokhrel ·

    量子联邦学习的稳定聚合方法

    arXiv:2609.00356v1 Announce Type: new Abstract: Quantum federated learning (QFL) enables clients to train quantum neural network (QNN) models without sharing private data. We find that aggregation in QFL is unstable under heterogeneous data, unreliable communication, variable fid…

  2. arXiv cs.LG TIER_1 English(EN) · Haruki Emori, Masaki Uchihara, Yuuki Tokunaga ·

    基于Bures--Uhlmann几何的量子联邦学习用于异构噪声客户端

    arXiv:2608.28379v1 Announce Type: cross Abstract: Quantum federated learning enables collaborative model training across quantum devices without sharing raw data, and it faces the data and hardware heterogeneity inherent to noisy quantum devices. Utilizing the quantum geometric t…