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arXiv · 2101.10657

Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing

Daniela A. Zaidenberg·Alessandro Sebastianelli·Dario Spiller·Bertrand Le Saux·Silvia Liberata Ullo
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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.

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BibTeXRIS

Daniela A. Zaidenberg, Alessandro Sebastianelli, Dario Spiller, Bertrand Le Saux, Silvia Liberata Ullo. 2021-01-26. Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing. https://arxiv.org/abs/2101.10657

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