Hauptseite > Publikationsdatenbank > A Single-Step Multiclass SVM Based on Quantum Annealing for Remote Sensing Data Classification > print

001     1018738
005     20250204113740.0
024 7 _ |2 doi
|a 10.1109/JSTARS.2023.3336926
024 7 _ |2 ISSN
|a 1939-1404
024 7 _ |2 ISSN
|a 2151-1535
024 7 _ |2 datacite_doi
|a 10.34734/FZJ-2023-05019
024 7 _ |a WOS:001127459900006
|2 WOS
037 _ _ |a FZJ-2023-05019
082 _ _ |a 520
100 1 _ |0 P:(DE-Juel1)191384
|a Delilbasic, Amer
|b 0
245 _ _ |a A Single-Step Multiclass SVM Based on Quantum Annealing for Remote Sensing Data Classification
260 _ _ |a New York, NY
|b IEEE
|c 2024
336 7 _ |2 DRIVER
|a article
336 7 _ |2 DataCite
|a Output Types/Journal article
336 7 _ |0 PUB:(DE-HGF)16
|2 PUB:(DE-HGF)
|a Journal Article
|b journal
|m journal
|s 1704188999_26656
336 7 _ |2 BibTeX
|a ARTICLE
336 7 _ |2 ORCID
|a JOURNAL_ARTICLE
336 7 _ |0 0
|2 EndNote
|a Journal Article
520 _ _ |a In recent years, the development of quantum annealers has enabled experimental demonstrations and has increased research interest in applications of quantum annealing, such as in quantum machine learning and in particular for the popular quantum Support Vector Machine (SVM). Several versions of the quantum SVM have been proposed, and quantum annealing has been shown to be effective in them. Extensions to multiclass problems have also been made, which consist of an ensemble of multiple binary classifiers. This work proposes a novel quantum SVM formulation for direct multiclass classification based on quantum annealing, called Quantum Multiclass SVM (QMSVM). The multiclass classification problem is formulated as a single quadratic unconstrained binary optimization problem solved with quantum annealing. The main objective of this work is to evaluate the feasibility, accuracy, and time performance of this approach. Experiments have been performed on the D-Wave Advantage quantum annealer for a classification problem on remote sensing data. Results indicate that, despite the memory demands of the quantum annealer, QMSVM can achieve an accuracy that is comparable to standard SVM methods, such as the one-versus-one (OVO), depending on the dataset (compared to OVO: 0.8663 vs 0.8598 on Toulouse, 0.8123 vs 0.8521 on Potsdam). More importantly, it scales much more efficiently with the number of training examples, resulting in nearly constant time (compared to OVO: 85.72s vs 248.02s on Toulouse, 58.89s vs 580.17s on Potsdam). This work shows an approach for bringing together classical and quantum computation, solving practical problems in remote sensing with current hardware.
536 _ _ |0 G:(DE-HGF)POF4-5111
|a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)
|c POF4-511
|f POF IV
|x 0
536 _ _ |0 G:(DE-HGF)POF4-5112
|a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)
|c POF4-511
|f POF IV
|x 1
536 _ _ |0 G:(EU-Grant)951733
|a RAISE - Research on AI- and Simulation-Based Engineering at Exascale (951733)
|c 951733
|f H2020-INFRAEDI-2019-1
|x 2
536 _ _ |0 G:(DE-Juel-1)DEA02266
|a EUROCC-2 (DEA02266)
|c DEA02266
|x 3
588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |0 P:(DE-HGF)0
|a Le Saux, Bertrand
|b 1
700 1 _ |0 P:(DE-Juel1)132239
|a Riedel, Morris
|b 2
|u fzj
700 1 _ |0 P:(DE-Juel1)138295
|a Michielsen, Kristel
|b 3
|u fzj
700 1 _ |0 P:(DE-Juel1)171343
|a Cavallaro, Gabriele
|b 4
773 _ _ |0 PERI:(DE-600)2457423-5
|a 10.1109/JSTARS.2023.3336926
|g p. 1 - 12
|p 1434 - 1445
|t IEEE journal of selected topics in applied earth observations and remote sensing
|v 17
|x 1939-1404
|y 2024
856 4 _ |u https://juser.fz-juelich.de/record/1018738/files/A_Single-Step_Multiclass_SVM_Based_on_Quantum_Annealing_for_Remote_Sensing_Data_Classification.pdf
|y OpenAccess
856 4 _ |u https://juser.fz-juelich.de/record/1018738/files/FINAL%20VERSION.pdf
|y OpenAccess
856 4 _ |u https://juser.fz-juelich.de/record/1018738/files/A_Single-Step_Multiclass_SVM_Based_on_Quantum_Annealing_for_Remote_Sensing_Data_Classification.gif?subformat=icon
|x icon
|y OpenAccess
856 4 _ |u https://juser.fz-juelich.de/record/1018738/files/A_Single-Step_Multiclass_SVM_Based_on_Quantum_Annealing_for_Remote_Sensing_Data_Classification.jpg?subformat=icon-1440
|x icon-1440
|y OpenAccess
856 4 _ |u https://juser.fz-juelich.de/record/1018738/files/A_Single-Step_Multiclass_SVM_Based_on_Quantum_Annealing_for_Remote_Sensing_Data_Classification.jpg?subformat=icon-180
|x icon-180
|y OpenAccess
856 4 _ |u https://juser.fz-juelich.de/record/1018738/files/A_Single-Step_Multiclass_SVM_Based_on_Quantum_Annealing_for_Remote_Sensing_Data_Classification.jpg?subformat=icon-640
|x icon-640
|y OpenAccess
856 4 _ |u https://juser.fz-juelich.de/record/1018738/files/FINAL%20VERSION.gif?subformat=icon
|x icon
|y OpenAccess
856 4 _ |u https://juser.fz-juelich.de/record/1018738/files/FINAL%20VERSION.jpg?subformat=icon-1440
|x icon-1440
|y OpenAccess
856 4 _ |u https://juser.fz-juelich.de/record/1018738/files/FINAL%20VERSION.jpg?subformat=icon-180
|x icon-180
|y OpenAccess
856 4 _ |u https://juser.fz-juelich.de/record/1018738/files/FINAL%20VERSION.jpg?subformat=icon-640
|x icon-640
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:1018738
|p openaire
|p open_access
|p OpenAPC
|p driver
|p VDB
|p ec_fundedresources
|p openCost
|p dnbdelivery
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)191384
|a Forschungszentrum Jülich
|b 0
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)132239
|a Forschungszentrum Jülich
|b 2
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)138295
|a Forschungszentrum Jülich
|b 3
|k FZJ
910 1 _ |0 I:(DE-588b)5008462-8
|6 P:(DE-Juel1)171343
|a Forschungszentrum Jülich
|b 4
|k FZJ
913 1 _ |0 G:(DE-HGF)POF4-511
|1 G:(DE-HGF)POF4-510
|2 G:(DE-HGF)POF4-500
|3 G:(DE-HGF)POF4
|4 G:(DE-HGF)POF
|9 G:(DE-HGF)POF4-5111
|a DE-HGF
|b Key Technologies
|l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action
|v Enabling Computational- & Data-Intensive Science and Engineering
|x 0
913 1 _ |0 G:(DE-HGF)POF4-511
|1 G:(DE-HGF)POF4-510
|2 G:(DE-HGF)POF4-500
|3 G:(DE-HGF)POF4
|4 G:(DE-HGF)POF
|9 G:(DE-HGF)POF4-5112
|a DE-HGF
|b Key Technologies
|l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action
|v Enabling Computational- & Data-Intensive Science and Engineering
|x 1
914 1 _ |y 2024
915 p c |0 PC:(DE-HGF)0000
|2 APC
|a APC keys set
915 p c |0 PC:(DE-HGF)0003
|2 APC
|a DOAJ Journal
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2023-10-25
915 _ _ |a Creative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 4.0
|0 LIC:(DE-HGF)CCBYNCND4
|2 HGFVOC
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2023-10-25
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2023-10-25
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2023-10-25
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2024-12-19
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2024-12-19
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2024-04-03T10:38:59Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2024-04-03T10:38:59Z
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Anonymous peer review
|d 2024-04-03T10:38:59Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2024-12-19
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1150
|2 StatID
|b Current Contents - Physical, Chemical and Earth Sciences
|d 2024-12-19
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2024-12-19
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b IEEE J-STARS : 2022
|d 2024-12-19
915 _ _ |a IF >= 5
|0 StatID:(DE-HGF)9905
|2 StatID
|b IEEE J-STARS : 2022
|d 2024-12-19
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 _ _ |a APC
980 _ _ |a UNRESTRICTED
980 1 _ |a APC
980 1 _ |a FullTexts

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21