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.

翻译：分布式量子计算是一种有前景的计算范式，能够执行超出单个量子设备能力范围的计算。在分布式量子计算中，隐私对于维护机密性以及在存在不可信计算节点时保护数据至关重要。本研究基于量子二分体相关器算法，提出了新型盲量子机器学习协议。我们的协议在降低通信开销的同时，保护了数据免受不可信方侵犯。我们引入了算法专属的鲁棒隐私保护机制，该机制计算开销低且无需复杂的密码学技术。随后，我们通过复杂度和隐私分析验证了所提协议的有效性。研究成果为分布式量子计算领域的发展铺平了道路，为量子技术时代隐私感知的机器学习应用开辟了新可能性。