On Quantum Methods for Machine Learning Problems Part I: Quantum Tools
This is a review of quantum methods for machine learning problems that consists of two parts. The first part, "quantum tools", presents the fundamentals of qubits, quantum registers, and quantum states, introduces important quantum tools based on known quantum search algorithms and SWAP-te...
| 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.2019.9020016 |
| _version_ | 1811250927073492992 |
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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", presents the fundamentals of qubits, quantum registers, and quantum states, introduces important quantum tools based on known quantum search algorithms and SWAP-test, and discusses the basic quantum procedures used for quantum search methods. The second part, "quantum classification algorithms", introduces several classification problems that can be accelerated by using quantum subroutines and discusses the quantum methods used for classification. |
| first_indexed | 2024-04-12T16:12:06Z |
| format | Article |
| id | doaj.art-b16ae8cc79954bb48ec530cf9423e6c5 |
| institution | Directory Open Access Journal |
| issn | 2096-0654 |
| language | English |
| last_indexed | 2024-04-12T16:12:06Z |
| publishDate | 2020-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj.art-b16ae8cc79954bb48ec530cf9423e6c52022-12-22T03:25:52ZengTsinghua University PressBig Data Mining and Analytics2096-06542020-03-0131415510.26599/BDMA.2019.9020016On Quantum Methods for Machine Learning Problems Part I: Quantum ToolsFarid 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", presents the fundamentals of qubits, quantum registers, and quantum states, introduces important quantum tools based on known quantum search algorithms and SWAP-test, and discusses the basic quantum procedures used for quantum search methods. The second part, "quantum classification algorithms", introduces several classification problems that can be accelerated by using quantum subroutines and discusses the quantum methods used for classification.https://www.sciopen.com/article/10.26599/BDMA.2019.9020016quantum algorithmquantum programmingmachine learning |
| spellingShingle | Farid Ablayev Marat Ablayev Joshua Zhexue Huang Kamil Khadiev Nailya Salikhova Dingming Wu On Quantum Methods for Machine Learning Problems Part I: Quantum Tools Big Data Mining and Analytics quantum algorithm quantum programming machine learning |
| title | On Quantum Methods for Machine Learning Problems Part I: Quantum Tools |
| title_full | On Quantum Methods for Machine Learning Problems Part I: Quantum Tools |
| title_fullStr | On Quantum Methods for Machine Learning Problems Part I: Quantum Tools |
| title_full_unstemmed | On Quantum Methods for Machine Learning Problems Part I: Quantum Tools |
| title_short | On Quantum Methods for Machine Learning Problems Part I: Quantum Tools |
| title_sort | on quantum methods for machine learning problems part i quantum tools |
| topic | quantum algorithm quantum programming machine learning |
| url | https://www.sciopen.com/article/10.26599/BDMA.2019.9020016 |
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