The privacy in classical federated learning can be breached through the use of local gradient results by using engineered queries from the clients. However, quantum communication channels are considered more secure because the use of measurements in the data causes some loss of information, which can be detected. Therefore, the quantum version of federated learning can be used to provide more privacy. Additionally, sending an $N$ dimensional data vector through a quantum channel requires sending $\log N$ entangled qubits, which can provide exponential efficiency if the data vector is obtained as quantum states. In this paper, we propose a quantum federated learning model where fixed design quantum chips are operated based on the quantum states sent by a centralized server. Based on the coming 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 as a quantum state is fed into client-side chips directly; therefore, it does not require measurements on the upcoming quantum state to obtain model parameters in order to compute gradients. This can provide efficiency over the models where parameter vector is sent via classical or quantum channels and local gradients are obtained through the obtained values of these parameters.

翻译：经典联邦学习中的隐私可能被利用客户端工程查询本地梯度结果而破坏。然而，量子通信信道因其测量过程会导致信息部分损失且可被检测的特性而被认为更为安全。因此，量子版本的联邦学习可用于提供更高程度的隐私保护。此外，通过量子信道传输一个N维数据向量仅需发送log N个纠缠量子比特，若数据向量以量子态形式获得，则可实现指数级效率提升。本文提出一种量子联邦学习模型，其中固定设计量子芯片基于中央服务器发送的量子态运行。客户端根据接收到的叠加态计算本地梯度，并以量子态形式将其发送至服务器，由服务器聚合以更新参数。由于服务器不发送模型参数，而是将算子以量子态形式发送，客户端无需共享模型。这使得异步学习模型的构建成为可能。此外，模型以量子状态直接输入客户端芯片，无需测量传入的量子态以获取模型参数来计算梯度。相较于通过经典或量子信道传输参数向量、并基于这些参数值计算本地梯度的模型，本方法可显著提升效率。