Quantum Support Vector Machine Based on Gradient Descent

Abstract Support vector machine is a supervised machine learning algorithm, which is usually solved by iterative method. Quantum algorithms have significant advantages over classical algorithms in terms of speed and capacity. In this work, a quantum support vector machine algorithm based on gradient...
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Gespeichert in:

Autor*in:	Li, Hong [verfasserIn] Jiang, Nan Zhang, Rui Wang, Zichen Wang, Hailiang

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Quantum support vector machine Quantum gradient descent Quantum machine learning Quantum computing

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022

Übergeordnetes Werk:	Enthalten in: International journal of theoretical physics - New York, NY : Springer Science + Business Media B.V., 1968, 61(2022), 3 vom: März
Übergeordnetes Werk:	volume:61 ; year:2022 ; number:3 ; month:03

Links:	Volltext

DOI / URN:	10.1007/s10773-022-05040-x

Katalog-ID:	SPR046616888

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520			\|a Abstract Support vector machine is a supervised machine learning algorithm, which is usually solved by iterative method. Quantum algorithms have significant advantages over classical algorithms in terms of speed and capacity. In this work, a quantum support vector machine algorithm based on gradient descent is proposed. Firstly, in the training stage, quantum gradient descent was used to obtain the training parameters, which reduces the time complexity of a single iteration from the classical polynomial level to the logarithmic level. Then, in the classification stage, we built a quantum classifier that can classify multiple samples at the same time, thereby improving the efficiency of classification. Finally, the simulation experiments proved the classification ability of our quantum support vector machine.
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allfields	10.1007/s10773-022-05040-x doi (DE-627)SPR046616888 (SPR)s10773-022-05040-x-e DE-627 ger DE-627 rakwb eng Li, Hong verfasserin aut Quantum Support Vector Machine Based on Gradient Descent 2022 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 2022 Abstract Support vector machine is a supervised machine learning algorithm, which is usually solved by iterative method. Quantum algorithms have significant advantages over classical algorithms in terms of speed and capacity. In this work, a quantum support vector machine algorithm based on gradient descent is proposed. Firstly, in the training stage, quantum gradient descent was used to obtain the training parameters, which reduces the time complexity of a single iteration from the classical polynomial level to the logarithmic level. Then, in the classification stage, we built a quantum classifier that can classify multiple samples at the same time, thereby improving the efficiency of classification. Finally, the simulation experiments proved the classification ability of our quantum support vector machine. Quantum support vector machine (dpeaa)DE-He213 Quantum gradient descent (dpeaa)DE-He213 Quantum machine learning (dpeaa)DE-He213 Quantum computing (dpeaa)DE-He213 Jiang, Nan (orcid)0000-0001-9269-8079 aut Zhang, Rui aut Wang, Zichen aut Wang, Hailiang aut Enthalten in International journal of theoretical physics New York, NY : Springer Science + Business Media B.V., 1968 61(2022), 3 vom: März (DE-627)320567966 (DE-600)2016168-2 1572-9575 nnns volume:61 year:2022 number:3 month:03 https://dx.doi.org/10.1007/s10773-022-05040-x 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_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_2119 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 61 2022 3 03
spelling	10.1007/s10773-022-05040-x doi (DE-627)SPR046616888 (SPR)s10773-022-05040-x-e DE-627 ger DE-627 rakwb eng Li, Hong verfasserin aut Quantum Support Vector Machine Based on Gradient Descent 2022 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 2022 Abstract Support vector machine is a supervised machine learning algorithm, which is usually solved by iterative method. Quantum algorithms have significant advantages over classical algorithms in terms of speed and capacity. In this work, a quantum support vector machine algorithm based on gradient descent is proposed. Firstly, in the training stage, quantum gradient descent was used to obtain the training parameters, which reduces the time complexity of a single iteration from the classical polynomial level to the logarithmic level. Then, in the classification stage, we built a quantum classifier that can classify multiple samples at the same time, thereby improving the efficiency of classification. Finally, the simulation experiments proved the classification ability of our quantum support vector machine. Quantum support vector machine (dpeaa)DE-He213 Quantum gradient descent (dpeaa)DE-He213 Quantum machine learning (dpeaa)DE-He213 Quantum computing (dpeaa)DE-He213 Jiang, Nan (orcid)0000-0001-9269-8079 aut Zhang, Rui aut Wang, Zichen aut Wang, Hailiang aut Enthalten in International journal of theoretical physics New York, NY : Springer Science + Business Media B.V., 1968 61(2022), 3 vom: März (DE-627)320567966 (DE-600)2016168-2 1572-9575 nnns volume:61 year:2022 number:3 month:03 https://dx.doi.org/10.1007/s10773-022-05040-x 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_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_2119 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 61 2022 3 03
allfields_unstemmed	10.1007/s10773-022-05040-x doi (DE-627)SPR046616888 (SPR)s10773-022-05040-x-e DE-627 ger DE-627 rakwb eng Li, Hong verfasserin aut Quantum Support Vector Machine Based on Gradient Descent 2022 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 2022 Abstract Support vector machine is a supervised machine learning algorithm, which is usually solved by iterative method. Quantum algorithms have significant advantages over classical algorithms in terms of speed and capacity. In this work, a quantum support vector machine algorithm based on gradient descent is proposed. Firstly, in the training stage, quantum gradient descent was used to obtain the training parameters, which reduces the time complexity of a single iteration from the classical polynomial level to the logarithmic level. Then, in the classification stage, we built a quantum classifier that can classify multiple samples at the same time, thereby improving the efficiency of classification. Finally, the simulation experiments proved the classification ability of our quantum support vector machine. Quantum support vector machine (dpeaa)DE-He213 Quantum gradient descent (dpeaa)DE-He213 Quantum machine learning (dpeaa)DE-He213 Quantum computing (dpeaa)DE-He213 Jiang, Nan (orcid)0000-0001-9269-8079 aut Zhang, Rui aut Wang, Zichen aut Wang, Hailiang aut Enthalten in International journal of theoretical physics New York, NY : Springer Science + Business Media B.V., 1968 61(2022), 3 vom: März (DE-627)320567966 (DE-600)2016168-2 1572-9575 nnns volume:61 year:2022 number:3 month:03 https://dx.doi.org/10.1007/s10773-022-05040-x 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_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_2119 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 61 2022 3 03
allfieldsGer	10.1007/s10773-022-05040-x doi (DE-627)SPR046616888 (SPR)s10773-022-05040-x-e DE-627 ger DE-627 rakwb eng Li, Hong verfasserin aut Quantum Support Vector Machine Based on Gradient Descent 2022 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 2022 Abstract Support vector machine is a supervised machine learning algorithm, which is usually solved by iterative method. Quantum algorithms have significant advantages over classical algorithms in terms of speed and capacity. In this work, a quantum support vector machine algorithm based on gradient descent is proposed. Firstly, in the training stage, quantum gradient descent was used to obtain the training parameters, which reduces the time complexity of a single iteration from the classical polynomial level to the logarithmic level. Then, in the classification stage, we built a quantum classifier that can classify multiple samples at the same time, thereby improving the efficiency of classification. Finally, the simulation experiments proved the classification ability of our quantum support vector machine. Quantum support vector machine (dpeaa)DE-He213 Quantum gradient descent (dpeaa)DE-He213 Quantum machine learning (dpeaa)DE-He213 Quantum computing (dpeaa)DE-He213 Jiang, Nan (orcid)0000-0001-9269-8079 aut Zhang, Rui aut Wang, Zichen aut Wang, Hailiang aut Enthalten in International journal of theoretical physics New York, NY : Springer Science + Business Media B.V., 1968 61(2022), 3 vom: März (DE-627)320567966 (DE-600)2016168-2 1572-9575 nnns volume:61 year:2022 number:3 month:03 https://dx.doi.org/10.1007/s10773-022-05040-x 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_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_2119 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 61 2022 3 03
allfieldsSound	10.1007/s10773-022-05040-x doi (DE-627)SPR046616888 (SPR)s10773-022-05040-x-e DE-627 ger DE-627 rakwb eng Li, Hong verfasserin aut Quantum Support Vector Machine Based on Gradient Descent 2022 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 2022 Abstract Support vector machine is a supervised machine learning algorithm, which is usually solved by iterative method. Quantum algorithms have significant advantages over classical algorithms in terms of speed and capacity. In this work, a quantum support vector machine algorithm based on gradient descent is proposed. Firstly, in the training stage, quantum gradient descent was used to obtain the training parameters, which reduces the time complexity of a single iteration from the classical polynomial level to the logarithmic level. Then, in the classification stage, we built a quantum classifier that can classify multiple samples at the same time, thereby improving the efficiency of classification. Finally, the simulation experiments proved the classification ability of our quantum support vector machine. Quantum support vector machine (dpeaa)DE-He213 Quantum gradient descent (dpeaa)DE-He213 Quantum machine learning (dpeaa)DE-He213 Quantum computing (dpeaa)DE-He213 Jiang, Nan (orcid)0000-0001-9269-8079 aut Zhang, Rui aut Wang, Zichen aut Wang, Hailiang aut Enthalten in International journal of theoretical physics New York, NY : Springer Science + Business Media B.V., 1968 61(2022), 3 vom: März (DE-627)320567966 (DE-600)2016168-2 1572-9575 nnns volume:61 year:2022 number:3 month:03 https://dx.doi.org/10.1007/s10773-022-05040-x 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_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_2119 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 61 2022 3 03
language	English
source	Enthalten in International journal of theoretical physics 61(2022), 3 vom: März volume:61 year:2022 number:3 month:03
sourceStr	Enthalten in International journal of theoretical physics 61(2022), 3 vom: März volume:61 year:2022 number:3 month:03
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Quantum support vector machine Quantum gradient descent Quantum machine learning Quantum computing
isfreeaccess_bool	false
container_title	International journal of theoretical physics
authorswithroles_txt_mv	Li, Hong @@aut@@ Jiang, Nan @@aut@@ Zhang, Rui @@aut@@ Wang, Zichen @@aut@@ Wang, Hailiang @@aut@@
publishDateDaySort_date	2022-03-01T00:00:00Z
hierarchy_top_id	320567966
id	SPR046616888
language_de	englisch
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author	Li, Hong
spellingShingle	Li, Hong misc Quantum support vector machine misc Quantum gradient descent misc Quantum machine learning misc Quantum computing Quantum Support Vector Machine Based on Gradient Descent
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topic_title	Quantum Support Vector Machine Based on Gradient Descent Quantum support vector machine (dpeaa)DE-He213 Quantum gradient descent (dpeaa)DE-He213 Quantum machine learning (dpeaa)DE-He213 Quantum computing (dpeaa)DE-He213
topic	misc Quantum support vector machine misc Quantum gradient descent misc Quantum machine learning misc Quantum computing
topic_unstemmed	misc Quantum support vector machine misc Quantum gradient descent misc Quantum machine learning misc Quantum computing
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title	Quantum Support Vector Machine Based on Gradient Descent
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title_full	Quantum Support Vector Machine Based on Gradient Descent
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title_sort	quantum support vector machine based on gradient descent
title_auth	Quantum Support Vector Machine Based on Gradient Descent
abstract	Abstract Support vector machine is a supervised machine learning algorithm, which is usually solved by iterative method. Quantum algorithms have significant advantages over classical algorithms in terms of speed and capacity. In this work, a quantum support vector machine algorithm based on gradient descent is proposed. Firstly, in the training stage, quantum gradient descent was used to obtain the training parameters, which reduces the time complexity of a single iteration from the classical polynomial level to the logarithmic level. Then, in the classification stage, we built a quantum classifier that can classify multiple samples at the same time, thereby improving the efficiency of classification. Finally, the simulation experiments proved the classification ability of our quantum support vector machine. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
abstractGer	Abstract Support vector machine is a supervised machine learning algorithm, which is usually solved by iterative method. Quantum algorithms have significant advantages over classical algorithms in terms of speed and capacity. In this work, a quantum support vector machine algorithm based on gradient descent is proposed. Firstly, in the training stage, quantum gradient descent was used to obtain the training parameters, which reduces the time complexity of a single iteration from the classical polynomial level to the logarithmic level. Then, in the classification stage, we built a quantum classifier that can classify multiple samples at the same time, thereby improving the efficiency of classification. Finally, the simulation experiments proved the classification ability of our quantum support vector machine. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
abstract_unstemmed	Abstract Support vector machine is a supervised machine learning algorithm, which is usually solved by iterative method. Quantum algorithms have significant advantages over classical algorithms in terms of speed and capacity. In this work, a quantum support vector machine algorithm based on gradient descent is proposed. Firstly, in the training stage, quantum gradient descent was used to obtain the training parameters, which reduces the time complexity of a single iteration from the classical polynomial level to the logarithmic level. Then, in the classification stage, we built a quantum classifier that can classify multiple samples at the same time, thereby improving the efficiency of classification. Finally, the simulation experiments proved the classification ability of our quantum support vector machine. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
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title_short	Quantum Support Vector Machine Based on Gradient Descent
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up_date	2024-07-03T23:33:43.584Z
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