论文标题
轻巧的量子联盟学习
Slimmable Quantum Federated Learning
论文作者
论文摘要
量子联合学习（QFL）最近受到了越来越多的关注，其中量子神经网络（QNN）集成到联邦学习（FL）中。与现有的静态QFL方法相反，我们在本文中提出了可靠的QFL（SLIMQFL），这是一个动态QFL框架，可以应对时变的通信通道和计算能量限制。通过利用QNN的独特性质，可以分别训练并动态利用其角度参数，从而使其可行。模拟结果证实了SLIMQFL的分类精度比香草QFL更高，尤其是在较差的通道条件下。
Quantum federated learning (QFL) has recently received increasing attention, where quantum neural networks (QNNs) are integrated into federated learning (FL). In contrast to the existing static QFL methods, we propose slimmable QFL (SlimQFL) in this article, which is a dynamic QFL framework that can cope with time-varying communication channels and computing energy limitations. This is made viable by leveraging the unique nature of a QNN where its angle parameters and pole parameters can be separately trained and dynamically exploited. Simulation results corroborate that SlimQFL achieves higher classification accuracy than Vanilla QFL, particularly under poor channel conditions on average.
