Distributed quantum computing, particularly distributed quantum machine
learning, has gained substantial prominence for its capacity to harness the
collective power of distributed quantum resources, transcending the limitations
of individual quantum nodes. Meanwhile, the critical concern of privacy within
distributed computing protocols remains a significant challenge, particularly
in standard classical federated learning (FL) scenarios where data of
participating clients is susceptible to leakage via gradient inversion attacks
by the server. This paper presents innovative quantum protocols with quantum
communication designed to address the FL problem, strengthen privacy measures,
and optimize communication efficiency. In contrast to previous works that
leverage expressive variational quantum circuits or differential privacy
techniques, we consider gradient information concealment using quantum states
and propose two distinct FL protocols, one based on private inner-product
estimation and the other on incremental learning. These protocols offer
substantial advancements in privacy preservation with low communication
resources, forging a path toward efficient quantum communication-assisted FL
protocols and contributing to the development of secure distributed quantum
machine learning, thus addressing critical privacy concerns in the quantum
computing era.

在分布式量子计算中，隐私保护一直是一个重要挑战，本文提出了两种基于量子状态的联邦学习协议，旨在优化隐私保护措施和通信效率，为高效的基于量子通信的联邦学习协议的发展以及安全的分布式量子机器学习做出了重要贡献。