Exploiting the Quantum Advantage for Satellite Image Processing: Review and Assessment
This article examines the current status of quantum computing (QC) in Earth observation 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...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Transactions on Quantum Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10339907/ |
| _version_ | 1797370474177495040 |
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| author | Soronzonbold Otgonbaatar Dieter Kranzlmuller |
| author_facet | Soronzonbold Otgonbaatar Dieter Kranzlmuller |
| author_sort | Soronzonbold Otgonbaatar |
| collection | DOAJ |
| description | This article examines the current status of quantum computing (QC) in Earth observation 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 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 with other satellite images since they have a limited number of input quantum bits and a small number of labeled benchmark images, making them less challenging to deploy on quantum computers. |
| first_indexed | 2024-03-08T18:02:09Z |
| format | Article |
| id | doaj.art-2925ac677de245b488ac4e7d0f8b520d |
| institution | Directory Open Access Journal |
| issn | 2689-1808 |
| language | English |
| last_indexed | 2024-03-08T18:02:09Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Quantum Engineering |
| spelling | doaj.art-2925ac677de245b488ac4e7d0f8b520d2024-01-02T00:03:04ZengIEEEIEEE Transactions on Quantum Engineering2689-18082024-01-0151910.1109/TQE.2023.333897010339907Exploiting the Quantum Advantage for Satellite Image Processing: Review and AssessmentSoronzonbold Otgonbaatar0https://orcid.org/0000-0001-7198-1350Dieter Kranzlmuller1https://orcid.org/0000-0002-8319-0123Remote Sensing Technology Institute, German Aerospace Center DLR, Weßling, GermanyInstitut für Informatik, Ludwig-Maximilians-Universität München, München, GermanyThis article examines the current status of quantum computing (QC) in Earth observation 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 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 with other satellite images since they have a limited number of input quantum bits and a small number of labeled benchmark images, making them less challenging to deploy on quantum computers.https://ieeexplore.ieee.org/document/10339907/Earth observation (EO)hyperspectral imagesimage classificationquantum computersquantum machine learning (QML)quantum resource estimation |
| spellingShingle | Soronzonbold Otgonbaatar Dieter Kranzlmuller Exploiting the Quantum Advantage for Satellite Image Processing: Review and Assessment IEEE Transactions on Quantum Engineering Earth observation (EO) hyperspectral images image classification quantum computers quantum machine learning (QML) quantum resource estimation |
| title | Exploiting the Quantum Advantage for Satellite Image Processing: Review and Assessment |
| title_full | Exploiting the Quantum Advantage for Satellite Image Processing: Review and Assessment |
| title_fullStr | Exploiting the Quantum Advantage for Satellite Image Processing: Review and Assessment |
| title_full_unstemmed | Exploiting the Quantum Advantage for Satellite Image Processing: Review and Assessment |
| title_short | Exploiting the Quantum Advantage for Satellite Image Processing: Review and Assessment |
| title_sort | exploiting the quantum advantage for satellite image processing review and assessment |
| topic | Earth observation (EO) hyperspectral images image classification quantum computers quantum machine learning (QML) quantum resource estimation |
| url | https://ieeexplore.ieee.org/document/10339907/ |
| work_keys_str_mv | AT soronzonboldotgonbaatar exploitingthequantumadvantageforsatelliteimageprocessingreviewandassessment AT dieterkranzlmuller exploitingthequantumadvantageforsatelliteimageprocessingreviewandassessment |