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Exploiting the Quantum Advantage for Satellite Image Processing: Review and Assessment

Otgonbaatar, Soronzonbold and Kranzlmüller, Dieter (2024) Exploiting the Quantum Advantage for Satellite Image Processing: Review and Assessment. IEEE Transactions on Quantum Engineering. IEEE - Institute of Electrical and Electronics Engineers. ISSN 2689-1808. (In Press)

[img] PDF - Preprint version (submitted draft)

Official URL: https://www.techrxiv.org/articles/preprint/Exploiting_the_Quantum_Advantage_for_Satellite_Image_Processing_Quantum_Resource_Estimation/24085122/1


This article examines the current status of quantum computing in Earth observation (EO) and satellite imagery. We analyze the potential limitations and applications of quantum learning models when dealing with satellite data, considering the persistent challenges of profiting from quantum advantage and finding the optimal sharing between high-performance computing (HPC) and quantum computing (QC). We then assess some parameterized quantum circuit models transpiled into a Clifford+T universal gate set. The T-gates shed light on the quantum resources required to deploy quantum models, either on an HPC system or several QC systems. In particular, if the T-gates cannot be simulated efficiently on an HPC system, we can apply a quantum computer and its computational power over conventional techniques. Our quantum resource estimation showed that quantum machine learning (QML) models, with a sufficient number of T-gates, provide the quantum advantage if and only if they generalize on unseen data points better than their classical counterparts deployed on the HPC system and they break the symmetry in their weights at each learning iteration like in conventional deep neural networks. We also estimated the quantum resources required for some QML models as an initial innovation. Lastly, we defined the optimal sharing between an HPC+QC system for executing QML models for hyperspectral satellite images. These are a unique dataset compared to other satellite images since they have a limited number of input qubits and a small number of labeled benchmark images, making them less challenging to deploy on quantum computers.

Item URL in elib:https://elib.dlr.de/199189/
Document Type:Article
Title:Exploiting the Quantum Advantage for Satellite Image Processing: Review and Assessment
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Date:2 January 2024
Journal or Publication Title:IEEE Transactions on Quantum Engineering
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:No
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Status:In Press
Keywords:quantum computing, quantum machine learning, quantum artificial intelligence, earth observation, remote sensing
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:15 Nov 2023 14:14
Last Modified:15 Nov 2023 14:14

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