The privacy in classical federated learning can be breached through the use of local gradient results combined with engineered queries to the clients. However, quantum communication channels are considered more secure because a measurement on the channel causes a loss of information, which can be detected by the sender. Therefore, the quantum version of federated learning can be used to provide better privacy. Additionally, sending an $N$-dimensional data vector through a quantum channel requires sending $\log N$ entangled qubits, which can potentially provide efficiency if the data vector is utilized as quantum states. In this paper, we propose a quantum federated learning model in which fixed design quantum chips are operated based on the quantum states sent by a centralized server. Based on the incoming superposition states, the clients compute and then send their local gradients as quantum states to the server, where they are aggregated to update parameters. Since the server does not send model parameters, but instead sends the operator as a quantum state, the clients are not required to share the model. This allows for the creation of asynchronous learning models. In addition, the model is fed into client-side chips directly as a quantum state; therefore, it does not require measurements on the incoming quantum state to obtain model parameters in order to compute gradients. This can provide efficiency over models where the parameter vector is sent via classical or quantum channels and local gradients are obtained through the obtained values these parameters.

翻译：经典联邦学习中的隐私可能通过利用本地梯度结果并结合对客户端的精心设计查询而被破坏。然而，量子通信信道被认为更为安全，因为对信道的测量会导致信息丢失，且发送方可以检测到这种丢失。因此，量子版本的联邦学习可用于提供更好的隐私保护。此外，通过量子信道发送一个$N$维数据向量需要传输$\log N$个纠缠量子比特，若数据向量以量子态形式被利用，则可能提供效率优势。本文提出一种量子联邦学习模型，其中固定设计的量子芯片基于中心服务器发送的量子态进行操作。客户端根据接收到的叠加态计算本地梯度，并将其作为量子态发送回服务器，在服务器端进行聚合以更新参数。由于服务器不发送模型参数，而是将算子作为量子态发送，客户端无需共享模型，从而支持构建异步学习模型。此外，模型直接以量子态形式输入客户端芯片，因此无需对输入的量子态进行测量以获得模型参数来计算梯度。相较于通过经典或量子信道发送参数向量、并基于获取的参数值计算本地梯度的模型，本方法可提供更高的效率。