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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20705 - Remote sensing

Result continuities

  • Project

  • Continuities

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

  • e-ISSN

  • 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