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。

隐私保护的量子联邦学习通过梯度隐藏

Privacy-preserving quantum federated learning via gradient hiding

Distributed quantum computing is a promising computational paradigm for
performing computations that are beyond the reach of individual quantum
devices. Privacy in distributed quantum computing is critical for maintaining
confidentiality and protecting the data in the presence of untrusted computing
nodes. In this work, we introduce novel blind quantum machine learning
protocols based on the quantum bipartite correlator algorithm. Our protocols
have reduced communication overhead while preserving the privacy of data from
untrusted parties. We introduce robust algorithm-specific privacy-preserving
mechanisms with low computational overhead that do not require complex
cryptographic techniques. We then validate the effectiveness of the proposed
protocols through complexity and privacy analysis. Our findings pave the way
for advancements in distributed quantum computing, opening up new possibilities
for privacy-aware machine learning applications in the era of quantum
technologies.

基于量子二分相干算法，我们介绍了新颖的盲量子机器学习协议，具有降低的通信开销，同时保持了来自不可信计算节点的数据隐私。我们引入了具有低计算开销的鲁棒算法特定的隐私保护机制，不需要复杂的密码学技术。通过复杂度和隐私分析，我们验证了提议协议的有效性。我们的研究结果为分布式量子计算铺平了道路，为量子技术时代的隐私感知机器学习应用开辟了新的可能性。

利用量子二分量子相关器进行盲量子机器学习

Blind quantum machine learning with quantum bipartite correlator

The Quantum Internet, by enabling quantum communications among remote quantum
nodes, is a network capable of supporting functionalities with no direct
counterpart in the classical world. Indeed, with the network and communications
functionalities provided by the Quantum Internet, remote quantum devices can
communicate and cooperate for solving challenging computational tasks by
adopting a distributed computing approach. The aim of this paper is to provide
the reader with an overview about the main challenges and open problems arising
with the design of a Distributed Quantum Computing ecosystem. For this, we
provide a survey, following a bottom-up approach, from a communications
engineering perspective. We start by introducing the Quantum Internet as the
fundamental underlying infrastructure of the Distributed Quantum Computing
ecosystem. Then we go further, by elaborating on a high-level system
abstraction of the Distributed Quantum Computing ecosystem. Such an abstraction
is described through a set of logical layers. Thereby, we clarify dependencies
among the aforementioned layers and, at the same time, a road-map emerges.

本文旨在从通信工程的角度出发，提供分布式量子计算生态系统的概述。通过逐层分析，我们解决了其中的主要挑战和潜在问题，并提出了一个路线图。