How Quantum Computing-Friendly Multispectral Data can be?

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F22%3A10251534" target="_blank" >RIV/61989100:27740/22:10251534 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9883676" target="_blank" >https://ieeexplore.ieee.org/document/9883676</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IGARSS46834.2022.9883676" target="_blank" >10.1109/IGARSS46834.2022.9883676</a>

Jazyk výsledku

angličtina

Název v původním jazyce

How Quantum Computing-Friendly Multispectral Data can be?

Popis výsledku v původním jazyce

Quantum computers consisting of hundreds of noisy qubits are already available and can run specific quantum algorithms although a large-scale fully error-corrected quantum computer is decades away. It is important to study their application to real-life computational problems. One such problem is Land Use and Land Cover classification of Earth Observation data set collected from the earth observation satellite mission using quantum machine learning methods. In this work, we compare the performance of the classical neural network on the re-labeled dataset of the Copernicus Sentinel-2 mission, when the model has access to Projected Quantum Kernel features. We show that classical neural net-work training accuracy increases drastically when the model has access to Projected Quantum Kernel features. This study shows the potential for quantum machine learning methods to Earth Observation data and provides key evidence for further investigation.

Název v anglickém jazyce

How Quantum Computing-Friendly Multispectral Data can be?

Popis výsledku anglicky

Quantum computers consisting of hundreds of noisy qubits are already available and can run specific quantum algorithms although a large-scale fully error-corrected quantum computer is decades away. It is important to study their application to real-life computational problems. One such problem is Land Use and Land Cover classification of Earth Observation data set collected from the earth observation satellite mission using quantum machine learning methods. In this work, we compare the performance of the classical neural network on the re-labeled dataset of the Copernicus Sentinel-2 mission, when the model has access to Projected Quantum Kernel features. We show that classical neural net-work training accuracy increases drastically when the model has access to Projected Quantum Kernel features. This study shows the potential for quantum machine learning methods to Earth Observation data and provides key evidence for further investigation.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20705 - Remote sensing

Projekt

—

Návaznosti

—

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium : Proceedings : 17-22 July, 2022, Kuala Lumpur, Malaysia

ISBN

978-1-66542-792-0

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

4153-4156

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Kuala Lumpur

Datum konání akce

17. 7. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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