A Single-Step Multiclass SVM Based on Quantum Annealing for Remote Sensing Data Classification
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...
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10329968/ |
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| author | Amer Delilbasic Bertrand Le Saux Morris Riedel Kristel Michielsen Gabriele Cavallaro |
| author_facet | Amer Delilbasic Bertrand Le Saux Morris Riedel Kristel Michielsen Gabriele Cavallaro |
| author_sort | Amer Delilbasic |
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| description | 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 article 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 article 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 versus 0.8598 on Toulouse, 0.8123 versus 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: 85.72 versus 248.02 s on Toulouse, 58.89 versus 580.17 s on Potsdam). This article shows an approach for bringing together classical and quantum computation, solving practical problems in remote sensing with current hardware. |
| format | Article |
| id | doaj-art-6796ef3db9fa4ff88b8b4ec7a46c1ce2 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-6796ef3db9fa4ff88b8b4ec7a46c1ce22025-08-20T04:03:25ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352024-01-01171434144510.1109/JSTARS.2023.333692610329968A Single-Step Multiclass SVM Based on Quantum Annealing for Remote Sensing Data ClassificationAmer Delilbasic0https://orcid.org/0000-0001-7845-5193Bertrand Le Saux1https://orcid.org/0000-0001-7162-6746Morris Riedel2https://orcid.org/0000-0003-1810-9330Kristel Michielsen3https://orcid.org/0000-0003-1444-4262Gabriele Cavallaro4https://orcid.org/0000-0002-3239-9904Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, GermanyΦ-Lab, European Space Research Institute, European Space Agency, Frascati, ItalyUniversity of Iceland, Reykjavik, IcelandJülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, GermanyUniversity of Iceland, Reykjavik, IcelandIn 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 article 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 article 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 versus 0.8598 on Toulouse, 0.8123 versus 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: 85.72 versus 248.02 s on Toulouse, 58.89 versus 580.17 s on Potsdam). This article shows an approach for bringing together classical and quantum computation, solving practical problems in remote sensing with current hardware.https://ieeexplore.ieee.org/document/10329968/Classificationquantum annealing (QA)quantum computing (QC)remote sensing (RS)support vector machine (SVM) |
| spellingShingle | Amer Delilbasic Bertrand Le Saux Morris Riedel Kristel Michielsen Gabriele Cavallaro A Single-Step Multiclass SVM Based on Quantum Annealing for Remote Sensing Data Classification IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Classification quantum annealing (QA) quantum computing (QC) remote sensing (RS) support vector machine (SVM) |
| title | A Single-Step Multiclass SVM Based on Quantum Annealing for Remote Sensing Data Classification |
| title_full | A Single-Step Multiclass SVM Based on Quantum Annealing for Remote Sensing Data Classification |
| title_fullStr | A Single-Step Multiclass SVM Based on Quantum Annealing for Remote Sensing Data Classification |
| title_full_unstemmed | A Single-Step Multiclass SVM Based on Quantum Annealing for Remote Sensing Data Classification |
| title_short | A Single-Step Multiclass SVM Based on Quantum Annealing for Remote Sensing Data Classification |
| title_sort | single step multiclass svm based on quantum annealing for remote sensing data classification |
| topic | Classification quantum annealing (QA) quantum computing (QC) remote sensing (RS) support vector machine (SVM) |
| url | https://ieeexplore.ieee.org/document/10329968/ |
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