Work

Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing

Year: 2021

Type: conference-paper

Abstract: 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 developments11QNN4EO repository: https://github.com/ESA-PhiLab/QNN4EO..

Source: IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

DOI: 10.1109/igarss47720.2021.9553133

Authors: Daniela A. Zaidenberg, Alessandro Sebastianelli, Dario Spiller, Bertrand Le Saux, Silvia Liberata Ullo

Institutions: Massachusetts Institute of Technology, University of Sannio, European Space Agency, European Space Research Institute, Agenzia Spaziale Italiana

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Massachusetts Institute of Technology

OpenAlex ID: W3122759250

FWCI: 2.444

Cites: 12

Cited by: 55

Related to: 10