Blind quantum machine learning with quantum bipartite correlator

TL;DR

This paper presents new privacy-preserving protocols for distributed quantum machine learning using the quantum bipartite correlator, reducing communication overhead and avoiding complex cryptography.

Contribution

It introduces novel blind quantum machine learning protocols based on the quantum bipartite correlator with low overhead and enhanced privacy in distributed quantum computing.

Findings

Protocols effectively preserve data privacy from untrusted parties

Reduced communication overhead compared to existing methods

Validated through complexity and privacy analysis

Abstract

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.