COMPARISON OF QUANTUM NEURAL NETWORK ALGORITHMS FOR EARTH OBSERVATION DATA CLASSIFICATION

被引：1

作者：

van Waveren, Matthijs ^{[1
]}

Savinaud, Mickael ^{[1
]}

Pasero, Guillaume ^{[1
]}

Defonte, Veronique ^{[1
]}

Brunet, Pierre-Marie ^{[2
]}

Faucoz, Orphee ^{[2
]}

Gawron, Piotr ^{[3
]}

Gardas, Bartlomiej ^{[4
]}

Puchala, Zbigniew ^{[4
]}

Pawela, Lukasz ^{[4
]}

机构：

[1] CS Grp, 6 Rue Brindejonc Moulinais, F-31506 Toulouse, France

[2] CNES, 10 Ave Edouard Belin, F-31401 Toulouse, France

[3] PAS, Nicolaus Copernicus Astron Ctr, AstroCeNT, Rektorska 4, PL-00614 Warsaw, Poland

[4] PAS, Inst Theoret & Appl Informat, Baltycka 5, PL-44100 Gliwice, Poland

来源：

IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年

关键词：

quantum machine learning; Earth observation; remote sensing; DEEP LEARNING BENCHMARK; LAND-USE; EUROSAT;

D O I：

10.1109/IGARSS52108.2023.10281429

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

This article describes a practical Earth Observation use case that would benefit from quantum computing. We analyze three quantum neural network algorithms. We implemented one of the algorithms on the EuroSAT dataset. We compare the algorithms with respect to complexity and degree of quantization. We believe that the algorithms we propose would be useful for the remote sensing community when quantum computing technologies become widely available.

引用

页码：780 / 783

页数：4

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