Quantum Machine Learning for Remote Sensing: Exploring potential and challenges
Abstract
The industry of quantum technologies is rapidly expanding, offering promising opportunities for various scientific domains. Among these emerging technologies, Quantum Machine Learning (QML) has attracted considerable attention due to its potential to revolutionize data processing and analysis. In this paper, we investigate the application of QML in the field of remote sensing. It is believed that QML can provide valuable insights for analysis of data from space. We delve into the common beliefs surrounding the quantum advantage in QML for remote sensing and highlight the open challenges that need to be addressed. To shed light on the challenges, we conduct a study focused on the problem of kernel value concentration, a phenomenon that adversely affects the runtime of quantum computers. Our findings indicate that while this issue negatively impacts quantum computer performance, it does not entirely negate the potential quantum advantage in QML for remote sensing.
Cite
@article{arxiv.2311.07626,
title = {Quantum Machine Learning for Remote Sensing: Exploring potential and challenges},
author = {Artur Miroszewski and Jakub Nalepa and Bertrand Le Saux and Jakub Mielczarek},
journal= {arXiv preprint arXiv:2311.07626},
year = {2023}
}
Comments
2 pages, 2 figures. Presented at the Big Data from Space 2023 conference