On Quantum Methods for Machine Learning Problems Part II: Quantum Classification Algorithms
This is a review of quantum methods for machine learning problems that consists of two parts. The first part, "quantum tools", presented some of the fundamentals and introduced several quantum tools based on known quantum search algorithms. This second part of the review presents several c...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2020-03-01
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| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2019.9020018 |
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| _version_ | 1832572979646562304 |
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| author | Farid Ablayev Marat Ablayev Joshua Zhexue Huang Kamil Khadiev Nailya Salikhova Dingming Wu |
| author_facet | Farid Ablayev Marat Ablayev Joshua Zhexue Huang Kamil Khadiev Nailya Salikhova Dingming Wu |
| author_sort | Farid Ablayev |
| collection | DOAJ |
| description | This is a review of quantum methods for machine learning problems that consists of two parts. The first part, "quantum tools", presented some of the fundamentals and introduced several quantum tools based on known quantum search algorithms. This second part of the review presents several classification problems in machine learning that can be accelerated with quantum subroutines. We have chosen supervised learning tasks as typical classification problems to illustrate the use of quantum methods for classification. |
| format | Article |
| id | doaj-art-f054b0272ae94394baf7cc0ea4245339 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2020-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-f054b0272ae94394baf7cc0ea42453392025-02-02T05:59:19ZengTsinghua University PressBig Data Mining and Analytics2096-06542020-03-0131566710.26599/BDMA.2019.9020018On Quantum Methods for Machine Learning Problems Part II: Quantum Classification AlgorithmsFarid Ablayev0Marat Ablayev1Joshua Zhexue Huang2Kamil Khadiev3Nailya Salikhova4Dingming Wu5<institution>Kazan Federal University</institution>, <city>Kazan</city> <postal-code>42008</postal-code>, <country>Russia</country>.<institution>Kazan Federal University</institution>, <city>Kazan</city> <postal-code>42008</postal-code>, <country>Russia</country>.<institution content-type="dept">College of Computer Science & Software Engineering</institution>, <institution>Shenzhen University</institution>, <city>Shenzhen</city> <postal-code>518000</postal-code>, <country>China</country>.<institution>Kazan Federal University</institution>, <city>Kazan</city> <postal-code>42008</postal-code>, <country>Russia</country>.<institution>Kazan Federal University</institution>, <city>Kazan</city> <postal-code>42008</postal-code>, <country>Russia</country>.<institution content-type="dept">College of Computer Science & Software Engineering</institution>, <institution>Shenzhen University</institution>, <city>Shenzhen</city> <postal-code>518000</postal-code>, <country>China</country>.This is a review of quantum methods for machine learning problems that consists of two parts. The first part, "quantum tools", presented some of the fundamentals and introduced several quantum tools based on known quantum search algorithms. This second part of the review presents several classification problems in machine learning that can be accelerated with quantum subroutines. We have chosen supervised learning tasks as typical classification problems to illustrate the use of quantum methods for classification.https://www.sciopen.com/article/10.26599/BDMA.2019.9020018quantum classificationbinary classificationnearest neighbor algorithm |
| spellingShingle | Farid Ablayev Marat Ablayev Joshua Zhexue Huang Kamil Khadiev Nailya Salikhova Dingming Wu On Quantum Methods for Machine Learning Problems Part II: Quantum Classification Algorithms Big Data Mining and Analytics quantum classification binary classification nearest neighbor algorithm |
| title | On Quantum Methods for Machine Learning Problems Part II: Quantum Classification Algorithms |
| title_full | On Quantum Methods for Machine Learning Problems Part II: Quantum Classification Algorithms |
| title_fullStr | On Quantum Methods for Machine Learning Problems Part II: Quantum Classification Algorithms |
| title_full_unstemmed | On Quantum Methods for Machine Learning Problems Part II: Quantum Classification Algorithms |
| title_short | On Quantum Methods for Machine Learning Problems Part II: Quantum Classification Algorithms |
| title_sort | on quantum methods for machine learning problems part ii quantum classification algorithms |
| topic | quantum classification binary classification nearest neighbor algorithm |
| url | https://www.sciopen.com/article/10.26599/BDMA.2019.9020018 |
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