Blind quantum machine learning with quantum bipartite correlator
Changhao Li Boning Li Omar Amer Ruslan Shaydulin Shouvanik Chakrabarti Guoqing Wang Haowei Xu Hao Tang Isidor Schoch Niraj Kumar Charles Lim Ju Li Paola Cappellaro Marco Pistoia
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.