How Quantum Computing-Friendly Multispectral Data can be?
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
How Quantum Computing-Friendly Multispectral Data can be?
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20705 - Remote sensing
Result continuities
Project
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Continuities
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Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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
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e-ISSN
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Number of pages
4
Pages from-to
4153-4156
Publisher name
IEEE
Place of publication
Piscataway
Event location
Kuala Lumpur
Event date
Jul 17, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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