A major concern of deep learning models is the large amount of data that is required to build and train them, much of which is reliant on sensitive and personally identifiable information that is vulnerable to access by third parties. Ideas of using the quantum internet to address this issue have been previously proposed, which would enable fast and completely secure online communications. Previous work has yielded a hybrid quantum-classical transfer learning scheme for classical data and communication with a hub-spoke topology. While quantum communication is secure from eavesdrop attacks and no measurements from quantum to classical translation, due to no cloning theorem, hub-spoke topology is not ideal for quantum communication without quantum memory. Here we seek to improve this model by implementing a decentralized ring topology for the federated learning scheme, where each client is given a portion of the entire dataset and only performs training on that set. We also demonstrate the first successful use of quantum weights for quantum federated learning, which allows us to perform our training entirely in quantum.

翻译：深度学习模型的一个主要问题在于构建和训练它们需要大量数据，其中许多数据依赖于敏感且可识别个人身份的信息，这些信息容易受到第三方访问。此前已有研究者提出利用量子互联网来解决这一问题，这将实现快速且完全安全的在线通信。先前的工作针对经典数据和通信提出了一种采用中心辐射型拓扑的混合量子-经典迁移学习方案。虽然量子通信能够抵御窃听攻击，且由于不可克隆定理，量子到经典的转换过程中无需进行测量，但中心辐射型拓扑在没有量子存储器的情况下并不适用于量子通信。本文旨在通过为联邦学习方案实现一种去中心化的环形拓扑来改进该模型，其中每个客户端获得全部数据集的一部分，并仅对该部分数据进行训练。我们还首次成功演示了将量子权重用于量子联邦学习，这使得我们能够完全在量子域内执行训练。