Enhancing quantum support vector machines through variational kernel training

Abstract We introduce a new model in quantum machine learning (QML) that combines the strengths of existing quantum kernel SVM (QK-SVM) and quantum variational SVM (QV-SVM) methods. Our proposed model, quantum variational kernel SVM (QVK-SVM), utilizes quantum kernel and quantum variational algorith...
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Autor*in:	Innan, N. [verfasserIn] Khan, M.A.Z. Panda, B. Bennai, M.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Quantum machine learning Quantum support vector machine Kernel Quantum variational algorithm Classification

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Quantum information processing - Dordrecht : Springer Science + Business Media B.V., 2002, 22(2023), 10 vom: 15. Okt.
Übergeordnetes Werk:	volume:22 ; year:2023 ; number:10 ; day:15 ; month:10

Links:	Volltext

DOI / URN:	10.1007/s11128-023-04138-3

Katalog-ID:	SPR053410661

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520			\|a Abstract We introduce a new model in quantum machine learning (QML) that combines the strengths of existing quantum kernel SVM (QK-SVM) and quantum variational SVM (QV-SVM) methods. Our proposed model, quantum variational kernel SVM (QVK-SVM), utilizes quantum kernel and quantum variational algorithms to improve accuracy in QML applications. In this paper, we conduct extensive experiments on the Iris dataset to evaluate the performance of QVK-SVM against QK-SVM and QV-SVM models. Our results demonstrate that QVK-SVM outperforms both existing models regarding accuracy, loss, and confusion matrix indicators. We believe that QVK-SVM can be a reliable and transformative tool for QML applications and recommend its use in future QML research.
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allfields	10.1007/s11128-023-04138-3 doi (DE-627)SPR053410661 (SPR)s11128-023-04138-3-e DE-627 ger DE-627 rakwb eng Innan, N. verfasserin (orcid)0000-0002-1014-3457 aut Enhancing quantum support vector machines through variational kernel training 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract We introduce a new model in quantum machine learning (QML) that combines the strengths of existing quantum kernel SVM (QK-SVM) and quantum variational SVM (QV-SVM) methods. Our proposed model, quantum variational kernel SVM (QVK-SVM), utilizes quantum kernel and quantum variational algorithms to improve accuracy in QML applications. In this paper, we conduct extensive experiments on the Iris dataset to evaluate the performance of QVK-SVM against QK-SVM and QV-SVM models. Our results demonstrate that QVK-SVM outperforms both existing models regarding accuracy, loss, and confusion matrix indicators. We believe that QVK-SVM can be a reliable and transformative tool for QML applications and recommend its use in future QML research. Quantum machine learning (dpeaa)DE-He213 Quantum support vector machine (dpeaa)DE-He213 Kernel (dpeaa)DE-He213 Quantum variational algorithm (dpeaa)DE-He213 Classification (dpeaa)DE-He213 Khan, M.A.Z. (orcid)0000-0002-1147-7782 aut Panda, B. aut Bennai, M. (orcid)0000-0002-7364-5171 aut Enthalten in Quantum information processing Dordrecht : Springer Science + Business Media B.V., 2002 22(2023), 10 vom: 15. Okt. (DE-627)354193031 (DE-600)2088114-9 1573-1332 nnns volume:22 year:2023 number:10 day:15 month:10 https://dx.doi.org/10.1007/s11128-023-04138-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2023 10 15 10
spelling	10.1007/s11128-023-04138-3 doi (DE-627)SPR053410661 (SPR)s11128-023-04138-3-e DE-627 ger DE-627 rakwb eng Innan, N. verfasserin (orcid)0000-0002-1014-3457 aut Enhancing quantum support vector machines through variational kernel training 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract We introduce a new model in quantum machine learning (QML) that combines the strengths of existing quantum kernel SVM (QK-SVM) and quantum variational SVM (QV-SVM) methods. Our proposed model, quantum variational kernel SVM (QVK-SVM), utilizes quantum kernel and quantum variational algorithms to improve accuracy in QML applications. In this paper, we conduct extensive experiments on the Iris dataset to evaluate the performance of QVK-SVM against QK-SVM and QV-SVM models. Our results demonstrate that QVK-SVM outperforms both existing models regarding accuracy, loss, and confusion matrix indicators. We believe that QVK-SVM can be a reliable and transformative tool for QML applications and recommend its use in future QML research. Quantum machine learning (dpeaa)DE-He213 Quantum support vector machine (dpeaa)DE-He213 Kernel (dpeaa)DE-He213 Quantum variational algorithm (dpeaa)DE-He213 Classification (dpeaa)DE-He213 Khan, M.A.Z. (orcid)0000-0002-1147-7782 aut Panda, B. aut Bennai, M. (orcid)0000-0002-7364-5171 aut Enthalten in Quantum information processing Dordrecht : Springer Science + Business Media B.V., 2002 22(2023), 10 vom: 15. Okt. (DE-627)354193031 (DE-600)2088114-9 1573-1332 nnns volume:22 year:2023 number:10 day:15 month:10 https://dx.doi.org/10.1007/s11128-023-04138-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2023 10 15 10
allfields_unstemmed	10.1007/s11128-023-04138-3 doi (DE-627)SPR053410661 (SPR)s11128-023-04138-3-e DE-627 ger DE-627 rakwb eng Innan, N. verfasserin (orcid)0000-0002-1014-3457 aut Enhancing quantum support vector machines through variational kernel training 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract We introduce a new model in quantum machine learning (QML) that combines the strengths of existing quantum kernel SVM (QK-SVM) and quantum variational SVM (QV-SVM) methods. Our proposed model, quantum variational kernel SVM (QVK-SVM), utilizes quantum kernel and quantum variational algorithms to improve accuracy in QML applications. In this paper, we conduct extensive experiments on the Iris dataset to evaluate the performance of QVK-SVM against QK-SVM and QV-SVM models. Our results demonstrate that QVK-SVM outperforms both existing models regarding accuracy, loss, and confusion matrix indicators. We believe that QVK-SVM can be a reliable and transformative tool for QML applications and recommend its use in future QML research. Quantum machine learning (dpeaa)DE-He213 Quantum support vector machine (dpeaa)DE-He213 Kernel (dpeaa)DE-He213 Quantum variational algorithm (dpeaa)DE-He213 Classification (dpeaa)DE-He213 Khan, M.A.Z. (orcid)0000-0002-1147-7782 aut Panda, B. aut Bennai, M. (orcid)0000-0002-7364-5171 aut Enthalten in Quantum information processing Dordrecht : Springer Science + Business Media B.V., 2002 22(2023), 10 vom: 15. Okt. (DE-627)354193031 (DE-600)2088114-9 1573-1332 nnns volume:22 year:2023 number:10 day:15 month:10 https://dx.doi.org/10.1007/s11128-023-04138-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2023 10 15 10
allfieldsGer	10.1007/s11128-023-04138-3 doi (DE-627)SPR053410661 (SPR)s11128-023-04138-3-e DE-627 ger DE-627 rakwb eng Innan, N. verfasserin (orcid)0000-0002-1014-3457 aut Enhancing quantum support vector machines through variational kernel training 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract We introduce a new model in quantum machine learning (QML) that combines the strengths of existing quantum kernel SVM (QK-SVM) and quantum variational SVM (QV-SVM) methods. Our proposed model, quantum variational kernel SVM (QVK-SVM), utilizes quantum kernel and quantum variational algorithms to improve accuracy in QML applications. In this paper, we conduct extensive experiments on the Iris dataset to evaluate the performance of QVK-SVM against QK-SVM and QV-SVM models. Our results demonstrate that QVK-SVM outperforms both existing models regarding accuracy, loss, and confusion matrix indicators. We believe that QVK-SVM can be a reliable and transformative tool for QML applications and recommend its use in future QML research. Quantum machine learning (dpeaa)DE-He213 Quantum support vector machine (dpeaa)DE-He213 Kernel (dpeaa)DE-He213 Quantum variational algorithm (dpeaa)DE-He213 Classification (dpeaa)DE-He213 Khan, M.A.Z. (orcid)0000-0002-1147-7782 aut Panda, B. aut Bennai, M. (orcid)0000-0002-7364-5171 aut Enthalten in Quantum information processing Dordrecht : Springer Science + Business Media B.V., 2002 22(2023), 10 vom: 15. Okt. (DE-627)354193031 (DE-600)2088114-9 1573-1332 nnns volume:22 year:2023 number:10 day:15 month:10 https://dx.doi.org/10.1007/s11128-023-04138-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2023 10 15 10
allfieldsSound	10.1007/s11128-023-04138-3 doi (DE-627)SPR053410661 (SPR)s11128-023-04138-3-e DE-627 ger DE-627 rakwb eng Innan, N. verfasserin (orcid)0000-0002-1014-3457 aut Enhancing quantum support vector machines through variational kernel training 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract We introduce a new model in quantum machine learning (QML) that combines the strengths of existing quantum kernel SVM (QK-SVM) and quantum variational SVM (QV-SVM) methods. Our proposed model, quantum variational kernel SVM (QVK-SVM), utilizes quantum kernel and quantum variational algorithms to improve accuracy in QML applications. In this paper, we conduct extensive experiments on the Iris dataset to evaluate the performance of QVK-SVM against QK-SVM and QV-SVM models. Our results demonstrate that QVK-SVM outperforms both existing models regarding accuracy, loss, and confusion matrix indicators. We believe that QVK-SVM can be a reliable and transformative tool for QML applications and recommend its use in future QML research. Quantum machine learning (dpeaa)DE-He213 Quantum support vector machine (dpeaa)DE-He213 Kernel (dpeaa)DE-He213 Quantum variational algorithm (dpeaa)DE-He213 Classification (dpeaa)DE-He213 Khan, M.A.Z. (orcid)0000-0002-1147-7782 aut Panda, B. aut Bennai, M. (orcid)0000-0002-7364-5171 aut Enthalten in Quantum information processing Dordrecht : Springer Science + Business Media B.V., 2002 22(2023), 10 vom: 15. Okt. (DE-627)354193031 (DE-600)2088114-9 1573-1332 nnns volume:22 year:2023 number:10 day:15 month:10 https://dx.doi.org/10.1007/s11128-023-04138-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2023 10 15 10
language	English
source	Enthalten in Quantum information processing 22(2023), 10 vom: 15. Okt. volume:22 year:2023 number:10 day:15 month:10
sourceStr	Enthalten in Quantum information processing 22(2023), 10 vom: 15. Okt. volume:22 year:2023 number:10 day:15 month:10
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institution	findex.gbv.de
topic_facet	Quantum machine learning Quantum support vector machine Kernel Quantum variational algorithm Classification
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authorswithroles_txt_mv	Innan, N. @@aut@@ Khan, M.A.Z. @@aut@@ Panda, B. @@aut@@ Bennai, M. @@aut@@
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author	Innan, N.
spellingShingle	Innan, N. misc Quantum machine learning misc Quantum support vector machine misc Kernel misc Quantum variational algorithm misc Classification Enhancing quantum support vector machines through variational kernel training
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topic_title	Enhancing quantum support vector machines through variational kernel training Quantum machine learning (dpeaa)DE-He213 Quantum support vector machine (dpeaa)DE-He213 Kernel (dpeaa)DE-He213 Quantum variational algorithm (dpeaa)DE-He213 Classification (dpeaa)DE-He213
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title_full	Enhancing quantum support vector machines through variational kernel training
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title_sort	enhancing quantum support vector machines through variational kernel training
title_auth	Enhancing quantum support vector machines through variational kernel training
abstract	Abstract We introduce a new model in quantum machine learning (QML) that combines the strengths of existing quantum kernel SVM (QK-SVM) and quantum variational SVM (QV-SVM) methods. Our proposed model, quantum variational kernel SVM (QVK-SVM), utilizes quantum kernel and quantum variational algorithms to improve accuracy in QML applications. In this paper, we conduct extensive experiments on the Iris dataset to evaluate the performance of QVK-SVM against QK-SVM and QV-SVM models. Our results demonstrate that QVK-SVM outperforms both existing models regarding accuracy, loss, and confusion matrix indicators. We believe that QVK-SVM can be a reliable and transformative tool for QML applications and recommend its use in future QML research. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract We introduce a new model in quantum machine learning (QML) that combines the strengths of existing quantum kernel SVM (QK-SVM) and quantum variational SVM (QV-SVM) methods. Our proposed model, quantum variational kernel SVM (QVK-SVM), utilizes quantum kernel and quantum variational algorithms to improve accuracy in QML applications. In this paper, we conduct extensive experiments on the Iris dataset to evaluate the performance of QVK-SVM against QK-SVM and QV-SVM models. Our results demonstrate that QVK-SVM outperforms both existing models regarding accuracy, loss, and confusion matrix indicators. We believe that QVK-SVM can be a reliable and transformative tool for QML applications and recommend its use in future QML research. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract We introduce a new model in quantum machine learning (QML) that combines the strengths of existing quantum kernel SVM (QK-SVM) and quantum variational SVM (QV-SVM) methods. Our proposed model, quantum variational kernel SVM (QVK-SVM), utilizes quantum kernel and quantum variational algorithms to improve accuracy in QML applications. In this paper, we conduct extensive experiments on the Iris dataset to evaluate the performance of QVK-SVM against QK-SVM and QV-SVM models. Our results demonstrate that QVK-SVM outperforms both existing models regarding accuracy, loss, and confusion matrix indicators. We believe that QVK-SVM can be a reliable and transformative tool for QML applications and recommend its use in future QML research. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Enhancing quantum support vector machines through variational kernel training
url	https://dx.doi.org/10.1007/s11128-023-04138-3
remote_bool	true
author2	Khan, M.A.Z. Panda, B. Bennai, M.
author2Str	Khan, M.A.Z. Panda, B. Bennai, M.
ppnlink	354193031
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doi_str	10.1007/s11128-023-04138-3
up_date	2024-07-03T19:19:28.164Z
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