Quantum Machine Learning for Earth observation

Otgonbaatar, Soronzonbold and Datcu, Mihai (2020) Quantum Machine Learning for Earth observation. Phi-Week 2020, 2020-09-28 - 2020-10-02, Online.

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Abstract

Our objectives are to employ the current methodologies and achievements made in emergent Quantum Computing to real-world problems in Earth observation; in particular, to enlarge and to broaden Earth Observation methodologies by using a D-Wave quantum annealer and a gate-based quantum computer.

Item URL in elib:

https://elib.dlr.de/141711/

Document Type:

Conference or Workshop Item (Poster)

Title:

Quantum Machine Learning for Earth observation

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Otgonbaatar, Soronzonbold	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Datcu, Mihai	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED

Date:

2020

Refereed publication:

Open Access:

Yes

Gold Open Access:

In SCOPUS:

In ISI Web of Science:

Status:

Published

Keywords:

D-Wave quantum annealer, gate-based quantum computer, Quantum Machine Learning (QML), Earth observation, remote sensing, InSAR, EuroSAT

Event Title:

Phi-Week 2020

Event Location:

Online

Event Type:

international Conference

Event Start Date:

28 September 2020

Event End Date:

2 October 2020

HGF - Research field:

Aeronautics, Space and Transport

HGF - Program:

Space

HGF - Program Themes:

Earth Observation

DLR - Research area:

Raumfahrt

DLR - Program:

R EO - Earth Observation

DLR - Research theme (Project):

R - Artificial Intelligence

Location:

Oberpfaffenhofen

Institutes and Institutions:

Remote Sensing Technology Institute > EO Data Science

Deposited By:

Otgonbaatar, Soronzonbold

Deposited On:

19 Apr 2021 12:27

Last Modified:

24 Apr 2024 20:41

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