Quantum Federated Learning: Remarks and Challenges
Author
Abstract

Quantum Computing Security 2022 - As the development of quantum computing hardware is on the rise, its potential application to various research areas has been investigated, including to machine learning. Recently, there have been several initiatives to expand the work to quantum federated learning (QFL). However, challenges arise due to the fact that quantum computation poses different characteristics from classical computation, giving an even more challenge for a federated setting. In this paper, we present a highlevel overview of the current state of research in QFL. Furthermore, we also describe in brief about quantum computation and discuss its present limitations in relation to QFL development. Additionally, possible approaches to deploy QFL are explored. Lastly, remarks and challenges of QFL are also presented.

Year of Publication
2022
Date Published
jun
Publisher
IEEE
Conference Location
Xi an, China
ISBN Number
978-1-66548-066-6
URL
https://ieeexplore.ieee.org/document/9842983/
DOI
10.1109/CSCloud-EdgeCom54986.2022.00010
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