Blind quantum machine learning with quantum bipartite correlator
This work addresses privacy concerns for users in distributed quantum computing, enabling secure machine learning applications, though it appears incremental as it builds on existing quantum algorithms.
The authors tackled the problem of privacy in distributed quantum computing by introducing blind quantum machine learning protocols based on the quantum bipartite correlator algorithm, achieving reduced communication overhead and robust privacy preservation without complex cryptographic techniques.
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.