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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| Autor*in: |
Li, Hong [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: International journal of theoretical physics - Springer US, 1968, 61(2022), 3 vom: März |
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| Übergeordnetes Werk: |
volume:61 ; year:2022 ; number:3 ; month:03 |
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| DOI / URN: |
10.1007/s10773-022-05040-x |
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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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10.1007/s10773-022-05040-x doi (DE-627)OLC207836181X (DE-He213)s10773-022-05040-x-p DE-627 ger DE-627 rakwb eng 530 VZ 33.00 bkl Li, Hong verfasserin aut Quantum Support Vector Machine Based on Gradient Descent 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc 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 Quantum gradient descent Quantum machine learning Quantum computing Jiang, Nan (orcid)0000-0001-9269-8079 aut Zhang, Rui aut Wang, Zichen aut Wang, Hailiang aut Enthalten in International journal of theoretical physics Springer US, 1968 61(2022), 3 vom: März (DE-627)129546097 (DE-600)218277-4 (DE-576)014996413 0020-7748 nnns volume:61 year:2022 number:3 month:03 https://doi.org/10.1007/s10773-022-05040-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY 33.00 VZ AR 61 2022 3 03 |
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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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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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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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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC207836181X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230506005325.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">221220s2022 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10773-022-05040-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC207836181X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10773-022-05040-x-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">530</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">33.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Hong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Quantum Support Vector Machine Based on Gradient Descent</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Support vector machine is a supervised machine learning algorithm, which is usually solved by iterative method. 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