Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing
Quantum Physics
2021-07-01 v3 Artificial Intelligence
Machine Learning
Abstract
This concept paper aims to provide a brief outline of quantum computers, explore existing methods of quantum image classification techniques, so focusing on remote sensing applications, and discuss the bottlenecks of performing these algorithms on currently available open source platforms. Initial results demonstrate feasibility. Next steps include expanding the size of the quantum hidden layer and increasing the variety of output image options.
Cite
@article{arxiv.2101.10657,
title = {Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing},
author = {Daniela A. Zaidenberg and Alessandro Sebastianelli and Dario Spiller and Bertrand Le Saux and Silvia Liberata Ullo},
journal= {arXiv preprint arXiv:2101.10657},
year = {2021}
}
Comments
Submitted and accepted for IEEE IGARSS2021