Otgonbaatar, Soronzonbold und Datcu, Mihai (2021) Quantum Annealer for Network Flow Minimization in InSAR Images. FRINGE 2021, 2021-05-31 - 2021-06-01, virtual.
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Offizielle URL: https://www.youtube.com/watch?v=nTy6_cwUrzI
Kurzfassung
Quantum Machine Learning is a young field of resarch, and it promises to provide the computational advantage over a conventional method for a family of problems in a practical domain. For practical problems in Earth observation (EO), the deluge of remotely-sensed images counting hundreds of Terabytes per day needs to be converted into meaningful information, largely impacting the socio-economic-environmental triangle. The particularity of EO images as “instrument” measurements extends the challenge of information extraction beyond the spatial information, as EO sensors gather physical parameters of a scene. Hence, our objectives are to enlarge the current methodologies and achievements made in emergent Quantum Machine Learning using a D-Wave quantum annealer as well as a gate-based quantum computer. As an exploratory work, we present the result obtained from the practical optimization problem in EO when appying a D-Wave quantum annealer.
elib-URL des Eintrags: | https://elib.dlr.de/142359/ | ||||||||||||
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Dokumentart: | Konferenzbeitrag (Poster) | ||||||||||||
Titel: | Quantum Annealer for Network Flow Minimization in InSAR Images | ||||||||||||
Autoren: |
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Datum: | Juni 2021 | ||||||||||||
Referierte Publikation: | Nein | ||||||||||||
Open Access: | Ja | ||||||||||||
Gold Open Access: | Nein | ||||||||||||
In SCOPUS: | Nein | ||||||||||||
In ISI Web of Science: | Nein | ||||||||||||
Status: | veröffentlicht | ||||||||||||
Stichwörter: | Quantum Machine Learning, D-Wave quantum annealer, optimization, Earth observation, InSAR | ||||||||||||
Veranstaltungstitel: | FRINGE 2021 | ||||||||||||
Veranstaltungsort: | virtual | ||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||
Veranstaltungsbeginn: | 31 Mai 2021 | ||||||||||||
Veranstaltungsende: | 1 Juni 2021 | ||||||||||||
Veranstalter : | ESA | ||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||
HGF - Programmthema: | Erdbeobachtung | ||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||
DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Künstliche Intelligenz | ||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||
Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > EO Data Science | ||||||||||||
Hinterlegt von: | Otgonbaatar, Soronzonbold | ||||||||||||
Hinterlegt am: | 28 Mai 2021 16:31 | ||||||||||||
Letzte Änderung: | 24 Apr 2024 20:42 |
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