Distributed Quantum Machine Learning

TL;DR

This paper introduces a method for distributed quantum machine learning that enables multiple parties to securely collaborate on quantum computations like addition and classification without revealing their data.

Contribution

It proposes a novel approach for secure, distributed quantum machine learning using shared entangled states, enabling privacy-preserving collaborative quantum algorithms.

Findings

Secure distributed quantum adder demonstrated

Distributed distance-based classifier developed

Enhanced data privacy in quantum computations

Abstract

Quantum computers can solve specific complex tasks for which no reasonable-time classical algorithm is known. Quantum computers do however also offer inherent security of data, as measurements destroy quantum states. Using shared entangled states, multiple parties can collaborate and securely compute quantum algorithms. In this paper we propose an approach for distributed quantum machine learning, which allows multiple parties to securely perform computations, without having to reveal their data. We will consider a distributed adder and a distributed distance-based classifier.