With the fast development of quantum computing and deep learning, quantum neural networks have attracted great attention recently. By leveraging the power of quantum computing, deep neural networks can potentially overcome computational power limitations in classic machine learning. However, when multiple quantum machines wish to train a global model using the local data on each machine, it may be very difficult to copy the data into one machine and train the model. Therefore, a collaborative quantum neural network framework is necessary. In this article, we borrow the core idea of federated learning to propose QuantumFed, a quantum federated learning framework to have multiple quantum nodes with local quantum data train a mode together. Our experiments show the feasibility and robustness of our framework.

中文翻译：

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QuantumFed：用于协作量子训练的联邦学习框架

随着量子计算和深度学习的快速发展，量子神经网络最近引起了极大的关注。通过利用量子计算的力量，深度神经网络有可能克服经典机器学习中的计算能力限制。然而，当多台量子机器希望使用每台机器上的本地数据训练一个全局模型时，将数据复制到一台机器上并训练模型可能会非常困难。因此，一个协作的量子神经网络框架是必要的。在本文中，我们借用联邦学习的核心思想提出QuantumFed，一个量子联邦学习框架，让多个量子节点与本地量子数据一起训练一个模式。我们的实验表明了我们框架的可行性和稳健性。