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.

通过在分布式学习方案中使用分散环拓扑结构和量子权重，实现针对深度学习模型的量子联邦学习，从而解决数据安全与训练效率问题。