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.

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通过梯度隐藏保护隐私的量子联合学习

分布式量子计算，特别是分布式量子机器学习，因其利用分布式量子资源的集体力量、超越单个量子节点的限制的能力而受到广泛关注。与此同时，分布式计算协议中的隐私问题仍然是一个重大挑战，特别是在标准经典联邦学习（FL）场景中，参与客户端的数据很容易通过服务器的梯度反转攻击而泄漏。本文提出了创新的量子协议和量子通信，旨在解决 FL 问题、加强隐私措施并优化通信效率。与之前利用表达变分量子电路或差分隐私技术的工作相比，我们考虑使用量子态的梯度信息隐藏，并提出两种不同的 FL 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的隐私保护方面取得了重大进展，为高效的量子通信辅助 FL 协议开辟了一条道路，并有助于安全分布式量子机器学习的发展，从而解决量子计算时代的关键隐私问题。