Quantum machine learning (QML) has emerged as a promising field that leans on
the developments in quantum computing to explore large complex machine learning
problems. Recently, some purely quantum machine learning models were proposed
such as the quantum convolutional neural networks (QCNN) to perform
classification on quantum data. However, all of the existing QML models rely on
centralized solutions that cannot scale well for large-scale and distributed
quantum networks. Hence, it is apropos to consider more practical quantum
federated learning (QFL) solutions tailored towards emerging quantum network
architectures. Indeed, developing QFL frameworks for quantum networks is
critical given the fragile nature of computing qubits and the difficulty of
transferring them. On top of its practical momentousness, QFL allows for
distributed quantum learning by leveraging existing wireless communication
infrastructure. This paper proposes the first fully quantum federated learning
framework that can operate over quantum data and, thus, share the learning of
quantum circuit parameters in a decentralized manner. First, given the lack of
existing quantum federated datasets in the literature, the proposed framework
begins by generating the first quantum federated dataset, with a hierarchical
data format, for distributed quantum networks. Then, clients sharing QCNN
models are fed with the quantum data to perform a classification task.
Subsequently, the server aggregates the learnable quantum circuit parameters
from clients and performs federated averaging. Extensive experiments are
conducted to evaluate and validate the effectiveness of the proposed QFL
solution. This work is the first to combine Google's TensorFlow Federated and
TensorFlow Quantum in a practical implementation.

本论文提出了第一个完全量子联邦学习框架，该框架可以在量子数据上运行并以分散的方式共享量子电路参数的学习。论文首先生成了第一个分层数据格式的量子联邦数据集，然后将其提供给使用 QCNN 模型的客户端执行分类任务，并对提出的 QFL 解决方案进行广泛的实验以评估和验证其有效性。