An all-pair quantum SVM approach for big data multiclass classification
In this paper, we discuss a quantum approach for the all-pair multiclass classification problem. In an all-pair approach, there is one binary classification problem for each pair of classes, and so there are k ( k − 1)/2 classifiers for a k -class classification problem. As compared to the classica...
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| Veröffentlicht in: | Quantum information processing 2018-10, Vol.17 (10), p.1-16, Article 282 |
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| creator | Bishwas, Arit Kumar Mani, Ashish Palade, Vasile |
| description | In this paper, we discuss a quantum approach for the all-pair multiclass classification problem. In an all-pair approach, there is one binary classification problem for each pair of classes, and so there are
k
(
k
− 1)/2 classifiers for a
k
-class classification problem. As compared to the classical multiclass support vector machine that can be implemented with polynomial run time complexity, our approach exhibits exponential speedup due to quantum computing. The quantum all-pair algorithm can also be used with other classification algorithms, and a speedup gain can be achieved as compared to their classical counterparts. |
| doi_str_mv | 10.1007/s11128-018-2046-z |
| format | Article |
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k
(
k
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k
-class classification problem. As compared to the classical multiclass support vector machine that can be implemented with polynomial run time complexity, our approach exhibits exponential speedup due to quantum computing. The quantum all-pair algorithm can also be used with other classification algorithms, and a speedup gain can be achieved as compared to their classical counterparts.</description><identifier>ISSN: 1570-0755</identifier><identifier>EISSN: 1573-1332</identifier><identifier>DOI: 10.1007/s11128-018-2046-z</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Algorithms ; Classification ; Data management ; Data Structures and Information Theory ; Mathematical Physics ; Physics ; Physics and Astronomy ; Quantum Computing ; Quantum Information Technology ; Quantum Physics ; Run time (computers) ; Spintronics ; Support vector machines</subject><ispartof>Quantum information processing, 2018-10, Vol.17 (10), p.1-16, Article 282</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2018</rights><rights>Copyright Springer Science & Business Media 2018</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-24894dfc685ff91d55ba34717df5df64a92028d2ff9afc4c743746137315fa433</citedby><cites>FETCH-LOGICAL-c316t-24894dfc685ff91d55ba34717df5df64a92028d2ff9afc4c743746137315fa433</cites><orcidid>0000-0001-8064-2035</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11128-018-2046-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11128-018-2046-z$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27922,27923,41486,42555,51317</link.rule.ids></links><search><creatorcontrib>Bishwas, Arit Kumar</creatorcontrib><creatorcontrib>Mani, Ashish</creatorcontrib><creatorcontrib>Palade, Vasile</creatorcontrib><title>An all-pair quantum SVM approach for big data multiclass classification</title><title>Quantum information processing</title><addtitle>Quantum Inf Process</addtitle><description>In this paper, we discuss a quantum approach for the all-pair multiclass classification problem. In an all-pair approach, there is one binary classification problem for each pair of classes, and so there are
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(
k
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k
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k
(
k
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k
-class classification problem. As compared to the classical multiclass support vector machine that can be implemented with polynomial run time complexity, our approach exhibits exponential speedup due to quantum computing. The quantum all-pair algorithm can also be used with other classification algorithms, and a speedup gain can be achieved as compared to their classical counterparts.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11128-018-2046-z</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0001-8064-2035</orcidid></addata></record> |
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| source | SpringerLink Journals - AutoHoldings |
| subjects | Algorithms Classification Data management Data Structures and Information Theory Mathematical Physics Physics Physics and Astronomy Quantum Computing Quantum Information Technology Quantum Physics Run time (computers) Spintronics Support vector machines |
| title | An all-pair quantum SVM approach for big data multiclass classification |
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