Dutta, Sreejit und Huber, Sigurd und Krieger, Gerhard (2024) On the potentials of Tensor-based Quantum Machine Learning for SAR land-cover classification. In: Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR. EUSAR 2024, 2024-04-23 - 2024-04-26, Munich, Germany. ISSN 2197-4403.
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Kurzfassung
Synthetic Aperture Radar (SAR) data, characterised by its high-dimensionality and complex spatial correlations, poses significant challenges in terms of efficient processing and meaningful interpretation. Classical algorithms, while effective, often struggle with the sheer volume and intricacy of the data. This paper introduces a novel approach employing tensor quantum machine learning (QML) to tackle the intricacies of SAR data. By harnessing the computational advantages of quantum mechanics and the representational efficiency of tensor networks, we try to achieve enhanced feature extraction and pattern recognition. We look at various Tensor decomposition schemes to reduce data dimensionality as well as Tensor based quantum circuits to perform land-cover classification. Preliminary results, based on simulations, demonstrate the potential of our tensor QML framework. For the scope of this research so far, we worked with simulated amplitude and phase data, but we will be applying the same for real world data in the future. This interdisciplinary study not only opens avenues for improved SAR data analysis but also enriches the burgeoning field of quantum machine learning by highlighting its applicability in remote sensing domains.
elib-URL des Eintrags: | https://elib.dlr.de/203287/ | ||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Poster) | ||||||||||||||||
Titel: | On the potentials of Tensor-based Quantum Machine Learning for SAR land-cover classification | ||||||||||||||||
Autoren: |
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Datum: | April 2024 | ||||||||||||||||
Erschienen in: | Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR | ||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||
Open Access: | Ja | ||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||
ISSN: | 2197-4403 | ||||||||||||||||
Status: | akzeptierter Beitrag | ||||||||||||||||
Stichwörter: | Quantum Computing, Machine Learning, Synthetic Aperture Radar, Land Cover classification, tensor networks | ||||||||||||||||
Veranstaltungstitel: | EUSAR 2024 | ||||||||||||||||
Veranstaltungsort: | Munich, Germany | ||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||
Veranstaltungsbeginn: | 23 April 2024 | ||||||||||||||||
Veranstaltungsende: | 26 April 2024 | ||||||||||||||||
Veranstalter : | VDE | ||||||||||||||||
HGF - Forschungsbereich: | keine Zuordnung | ||||||||||||||||
HGF - Programm: | keine Zuordnung | ||||||||||||||||
HGF - Programmthema: | keine Zuordnung | ||||||||||||||||
DLR - Schwerpunkt: | Quantencomputing-Initiative | ||||||||||||||||
DLR - Forschungsgebiet: | QC AW - Anwendungen | ||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | QC - QUA-SAR | ||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||
Institute & Einrichtungen: | Institut für Hochfrequenztechnik und Radarsysteme | ||||||||||||||||
Hinterlegt von: | Dutta, Sreejit | ||||||||||||||||
Hinterlegt am: | 19 Mär 2024 15:18 | ||||||||||||||||
Letzte Änderung: | 01 Jun 2024 03:00 |
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