Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing
Daniela A. Zaidenberg, Alessandro Sebastianelli, Dario Spiller, Bertrand Le Saux, Silvia Liberata Ullo · IRIS Research product catalog (Sapienza University of Rome) · 2021
This article aims to explore the potential of current approaches for quantum image classification in the context of remote sensing. After a brief outline of quantum computers and an analysis of the current bottlenecks, it shows for the first time experiments with quantum neural networks on a reference Earth observation (EO) dataset: EuroSAT. Moreover, it establishes the proof of concept of quantum computing for EO: the models trained and run on a quantum simulator are on par with classical ones. We make the open-source code available for further developments.