Quantum Machine Learning: Recent Advances, Challenges, and Perspectives
This study presents a comprehensive survey on Quantum Machine Learning (QML) along with its current status, challenges, and perspectives. QML combines quantum computing and machine learning to solve complex problems in different domains, leveraging quantum algorithms to enhance classical machine lea...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11014055/ |
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| author | Pradeep Lamichhane Danda B. Rawat |
| author_facet | Pradeep Lamichhane Danda B. Rawat |
| author_sort | Pradeep Lamichhane |
| collection | DOAJ |
| description | This study presents a comprehensive survey on Quantum Machine Learning (QML) along with its current status, challenges, and perspectives. QML combines quantum computing and machine learning to solve complex problems in different domains, leveraging quantum algorithms to enhance classical machine learning techniques. We explore the application of QML in various domains such as cybersecurity, finance, healthcare, and drug discovery. The survey includes detailed tabular comparisons of the different QML models used for each application area, highlighting key techniques, findings, and their limitations. In this work, we identify important trends such as the strong potential of hybrid quantum-classical models for near-term applications and the significant challenges in the quantum domain due to quantum noise, limited qubit scalability, and costly qRAM implementations. Furthermore, we discuss solutions that emphasize advances in hardware, quantum error correction, and algorithmic innovations to address these challenges. By providing an in-depth analysis of QML’s potential across different fields, this study provides valuable insights into how QML can address complex real-world challenges and transform traditional machine learning practices. |
| format | Article |
| id | doaj-art-be4375e0134545f5b8d589bbb101ee55 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-be4375e0134545f5b8d589bbb101ee552025-08-20T03:24:36ZengIEEEIEEE Access2169-35362025-01-0113940579410510.1109/ACCESS.2025.357324411014055Quantum Machine Learning: Recent Advances, Challenges, and PerspectivesPradeep Lamichhane0https://orcid.org/0009-0003-9348-1498Danda B. Rawat1https://orcid.org/0000-0003-3638-3464Department of Electrical Engineering and Computer Science, Howard University, Washington, DC, USADepartment of Electrical Engineering and Computer Science, Howard University, Washington, DC, USAThis study presents a comprehensive survey on Quantum Machine Learning (QML) along with its current status, challenges, and perspectives. QML combines quantum computing and machine learning to solve complex problems in different domains, leveraging quantum algorithms to enhance classical machine learning techniques. We explore the application of QML in various domains such as cybersecurity, finance, healthcare, and drug discovery. The survey includes detailed tabular comparisons of the different QML models used for each application area, highlighting key techniques, findings, and their limitations. In this work, we identify important trends such as the strong potential of hybrid quantum-classical models for near-term applications and the significant challenges in the quantum domain due to quantum noise, limited qubit scalability, and costly qRAM implementations. Furthermore, we discuss solutions that emphasize advances in hardware, quantum error correction, and algorithmic innovations to address these challenges. By providing an in-depth analysis of QML’s potential across different fields, this study provides valuable insights into how QML can address complex real-world challenges and transform traditional machine learning practices.https://ieeexplore.ieee.org/document/11014055/Quantum machine learningquantum computingquantum modelsquantum neural networksquantum support vector machineshybrid quantum-classical models |
| spellingShingle | Pradeep Lamichhane Danda B. Rawat Quantum Machine Learning: Recent Advances, Challenges, and Perspectives IEEE Access Quantum machine learning quantum computing quantum models quantum neural networks quantum support vector machines hybrid quantum-classical models |
| title | Quantum Machine Learning: Recent Advances, Challenges, and Perspectives |
| title_full | Quantum Machine Learning: Recent Advances, Challenges, and Perspectives |
| title_fullStr | Quantum Machine Learning: Recent Advances, Challenges, and Perspectives |
| title_full_unstemmed | Quantum Machine Learning: Recent Advances, Challenges, and Perspectives |
| title_short | Quantum Machine Learning: Recent Advances, Challenges, and Perspectives |
| title_sort | quantum machine learning recent advances challenges and perspectives |
| topic | Quantum machine learning quantum computing quantum models quantum neural networks quantum support vector machines hybrid quantum-classical models |
| url | https://ieeexplore.ieee.org/document/11014055/ |
| work_keys_str_mv | AT pradeeplamichhane quantummachinelearningrecentadvanceschallengesandperspectives AT dandabrawat quantummachinelearningrecentadvanceschallengesandperspectives |