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.

翻译：分布式量子计算，特别是分布式量子机器学习，因其能够整合分布式量子资源的集体力量、突破单个量子节点的局限性而日益受到关注。同时，分布式计算协议中的隐私关键问题仍是一项重大挑战，尤其是在标准的经典联邦学习场景中，参与客户的数据可能通过服务器的梯度反转攻击而泄露。本文提出了创新的量子通信协议，旨在解决联邦学习问题、强化隐私保护措施并优化通信效率。与以往利用表达性变分量子电路或差分隐私技术的工作不同，我们考虑使用量子态来隐藏梯度信息，并提出了两种不同的联邦学习协议：一种基于私有内积估计，另一种基于增量学习。这些协议在低通信资源下实现了隐私保护的重大进展，为高效的量子通信辅助联邦学习协议开辟了道路，并促进了安全分布式量子机器学习的发展，从而解决了量子计算时代的关键隐私问题。