Related papers: Distributed Quantum Machine Learning

Distributed Quantum Machine Learning

URL: http://arxiv.org/abs/2208.10316v1
Date: Mon, 22 Aug 2022 13:52:21 GMT
Title: Distributed Quantum Machine Learning
Authors: Niels M. P. Neumann, Robert S. Wezeman
Abstract summary: Quantum computers offer inherent security of data, as measurements destroy quantum states. We propose an approach for distributed quantum machine learning, which allows multiple parties to collaborate and securely compute quantum algorithms.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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