Chapter 3 Quantum machine learning algorithms: a comprehensive review
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Juhi Singh
Abstract
Quantum machine learning (QML) combined with classical machine learning provides hitherto unexplored computational capabilities for problem resolution. The goal of this chapter is to present a thorough analysis of the ideas and uses of quantum computing capacity on machine learning methods. A full analysis of popular QML algorithms, such as quantum principal component analysis, quantum support vector machines, and quantum neural networks, is conducted after an explanation of the foundations of the quantum concept and its distinguishing features. The critical assessment considers robustness, interpretability, and scalability in addition to evaluating the benefits and drawbacks of each method. The primary focus is on the merging of classical and quantum models, looking at areas of convergence that make use of the most advantageous aspects of both paradigms. This work addresses challenges associated with putting quantum algorithms into practice, including hardware constraints and error-reduction strategies. This chapter evaluates the state of quantum computing technologies and their applicability to real-world quantum machine learning applications, in addition to reviewing particular algorithms. This is jam-packed with cutting-edge discoveries and trends that shed light on where QML research will go in the future. The final section of this chapter discusses potential future paths for QML, emphasizing the need for unified benchmarks, enhanced quantum hardware, and cooperative efforts between the machine learning and quantum computing groups. This concise review attempts to serve as a useful reference for scholars, professionals, and hobbyists navigating the quickly evolving field of quantum-enhanced machine learning by providing an extensive overview of QML algorithms.
Abstract
Quantum machine learning (QML) combined with classical machine learning provides hitherto unexplored computational capabilities for problem resolution. The goal of this chapter is to present a thorough analysis of the ideas and uses of quantum computing capacity on machine learning methods. A full analysis of popular QML algorithms, such as quantum principal component analysis, quantum support vector machines, and quantum neural networks, is conducted after an explanation of the foundations of the quantum concept and its distinguishing features. The critical assessment considers robustness, interpretability, and scalability in addition to evaluating the benefits and drawbacks of each method. The primary focus is on the merging of classical and quantum models, looking at areas of convergence that make use of the most advantageous aspects of both paradigms. This work addresses challenges associated with putting quantum algorithms into practice, including hardware constraints and error-reduction strategies. This chapter evaluates the state of quantum computing technologies and their applicability to real-world quantum machine learning applications, in addition to reviewing particular algorithms. This is jam-packed with cutting-edge discoveries and trends that shed light on where QML research will go in the future. The final section of this chapter discusses potential future paths for QML, emphasizing the need for unified benchmarks, enhanced quantum hardware, and cooperative efforts between the machine learning and quantum computing groups. This concise review attempts to serve as a useful reference for scholars, professionals, and hobbyists navigating the quickly evolving field of quantum-enhanced machine learning by providing an extensive overview of QML algorithms.
Kapitel in diesem Buch
- Frontmatter I
- Contents V
- List of contributors VII
- Chapter 1 Quantum computing in society: impacts and implications 1
- Chapter 2 Quantum computing with machine learning: opportunities and challenges 19
- Chapter 3 Quantum machine learning algorithms: a comprehensive review 37
- Chapter 4 Highlighting major issues with quantum computing in healthcare 53
- Chapter 5 Privacy and security for 6G’s IoT-connected future in the age of quantum computing 67
- Chapter 6 Can quantum computers revolutionize health systems? 87
- Chapter 7 Industrial automation and quantum computing 105
- Chapter 8 Applications of quantum computing in financial planning and financial control 127
- Chapter 9 Quantum computing in machine learning: an overview 141
- Chapter 10 The impact of AI and automation on income inequality in BRICS countries and the role of structural factors and women’s empowerment 155
- Chapter 11 Quantum computing and machine learning: a symbiotic relationship 177
- Chapter 12 Quantum-secured healthcare data and cybersecurity innovations in the era of Industry 5.0 199
- Chapter 13 Introduction to quantum computing and its revolution in industry and society 219
- Chapter 14 Advancing healthcare through the opportunities and challenges of quantum computing 239
- Chapter 15 Quantum computing in drug and chemical 255
- Index 279
Kapitel in diesem Buch
- Frontmatter I
- Contents V
- List of contributors VII
- Chapter 1 Quantum computing in society: impacts and implications 1
- Chapter 2 Quantum computing with machine learning: opportunities and challenges 19
- Chapter 3 Quantum machine learning algorithms: a comprehensive review 37
- Chapter 4 Highlighting major issues with quantum computing in healthcare 53
- Chapter 5 Privacy and security for 6G’s IoT-connected future in the age of quantum computing 67
- Chapter 6 Can quantum computers revolutionize health systems? 87
- Chapter 7 Industrial automation and quantum computing 105
- Chapter 8 Applications of quantum computing in financial planning and financial control 127
- Chapter 9 Quantum computing in machine learning: an overview 141
- Chapter 10 The impact of AI and automation on income inequality in BRICS countries and the role of structural factors and women’s empowerment 155
- Chapter 11 Quantum computing and machine learning: a symbiotic relationship 177
- Chapter 12 Quantum-secured healthcare data and cybersecurity innovations in the era of Industry 5.0 199
- Chapter 13 Introduction to quantum computing and its revolution in industry and society 219
- Chapter 14 Advancing healthcare through the opportunities and challenges of quantum computing 239
- Chapter 15 Quantum computing in drug and chemical 255
- Index 279
