Design and analysis of quantum machine learning: a survey
Machine learning has demonstrated tremendous potential in solving real-world problems. However, with the exponential growth of data amount and the increase of model complexity, the processing efficiency of machine learning declines rapidly. Meanwhile, the emergence of quantum computing has given ris...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Connection Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/09540091.2024.2312121 |
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| _version_ | 1849763828365000704 |
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| author | Linshu Chen Tao Li Yuxiang Chen Xiaoyan Chen Marcin Wozniak Neal Xiong Wei Liang |
| author_facet | Linshu Chen Tao Li Yuxiang Chen Xiaoyan Chen Marcin Wozniak Neal Xiong Wei Liang |
| author_sort | Linshu Chen |
| collection | DOAJ |
| description | Machine learning has demonstrated tremendous potential in solving real-world problems. However, with the exponential growth of data amount and the increase of model complexity, the processing efficiency of machine learning declines rapidly. Meanwhile, the emergence of quantum computing has given rise to quantum machine learning, which relies on superposition and entanglement, exhibiting exponential optimisation compared to traditional machine learning. Therefore, in the paper, we survey the basic concepts, algorithms, applications and challenges of quantum machine learning. Concretely, we first review the basic concepts of quantum computing including qubit, quantum gates, quantum entanglement, etc.. Secondly, we in-depth discuss 5 quantum machine learning algorithms of quantum support vector machine, quantum neural network, quantum k-nearest neighbour, quantum principal component analysis and quantum k-Means algorithm. Thirdly, we conduct discussions on the applications of quantum machine learning in image recognition, drug efficacy prediction and cybersecurity. Finally, we summarise the challenges of quantum machine learning consisting of algorithm design, hardware limitations, data encoding, quantum landscapes, noise and decoherence. |
| format | Article |
| id | doaj-art-bffd79b3d4154bf0bd91581705e4a391 |
| institution | DOAJ |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-bffd79b3d4154bf0bd91581705e4a3912025-08-20T03:05:17ZengTaylor & Francis GroupConnection Science0954-00911360-04942024-12-0136110.1080/09540091.2024.2312121Design and analysis of quantum machine learning: a surveyLinshu Chen0Tao Li1Yuxiang Chen2Xiaoyan Chen3Marcin Wozniak4Neal Xiong5Wei Liang6School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, People’s Republic of ChinaSchool of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, People’s Republic of ChinaSchool of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, People’s Republic of ChinaSchool of Software Engineering, University of Technology, Xiamen, People’s Republic of ChinaFaculty of Applied Mathematics, Silesian University of Technology, Gliwice, PolandSchool of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, People’s Republic of ChinaSchool of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, People’s Republic of ChinaMachine learning has demonstrated tremendous potential in solving real-world problems. However, with the exponential growth of data amount and the increase of model complexity, the processing efficiency of machine learning declines rapidly. Meanwhile, the emergence of quantum computing has given rise to quantum machine learning, which relies on superposition and entanglement, exhibiting exponential optimisation compared to traditional machine learning. Therefore, in the paper, we survey the basic concepts, algorithms, applications and challenges of quantum machine learning. Concretely, we first review the basic concepts of quantum computing including qubit, quantum gates, quantum entanglement, etc.. Secondly, we in-depth discuss 5 quantum machine learning algorithms of quantum support vector machine, quantum neural network, quantum k-nearest neighbour, quantum principal component analysis and quantum k-Means algorithm. Thirdly, we conduct discussions on the applications of quantum machine learning in image recognition, drug efficacy prediction and cybersecurity. Finally, we summarise the challenges of quantum machine learning consisting of algorithm design, hardware limitations, data encoding, quantum landscapes, noise and decoherence.https://www.tandfonline.com/doi/10.1080/09540091.2024.2312121Machine learningquantum computingquantum entanglementquantum machine learningquantum neural networks |
| spellingShingle | Linshu Chen Tao Li Yuxiang Chen Xiaoyan Chen Marcin Wozniak Neal Xiong Wei Liang Design and analysis of quantum machine learning: a survey Connection Science Machine learning quantum computing quantum entanglement quantum machine learning quantum neural networks |
| title | Design and analysis of quantum machine learning: a survey |
| title_full | Design and analysis of quantum machine learning: a survey |
| title_fullStr | Design and analysis of quantum machine learning: a survey |
| title_full_unstemmed | Design and analysis of quantum machine learning: a survey |
| title_short | Design and analysis of quantum machine learning: a survey |
| title_sort | design and analysis of quantum machine learning a survey |
| topic | Machine learning quantum computing quantum entanglement quantum machine learning quantum neural networks |
| url | https://www.tandfonline.com/doi/10.1080/09540091.2024.2312121 |
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