Book contents
- Frontmatter
- Dedication
- Contents
- Acknowledgments
- Introduction
- 1 The Mathematical Minimum
- 2 Quantum Computing Fundamentals
- 3 Simulation Infrastructure
- 4 Quantum Tools and Techniques
- 5 Beyond Classical
- 6 Algorithms Exploiting Entanglement
- 7 State Similarity Tests
- 8 Black-Box Algorithms
- 9 State Preparation
- 10 Algorithms Using Amplitude Amplification
- 11 Algorithms Using Quantum Fourier Transform
- 12 Quantum Walk Algorithms
- 13 Optimization Algorithms
- 14 Quantum Machine Learning
- 15 Quantum Error Correction
- 16 Quantum Languages, Compilers, and Tools
- Appendix Sparse Implementation
- References
- Index
14 - Quantum Machine Learning
Published online by Cambridge University Press: 24 December 2025
Book contents
- Frontmatter
- Dedication
- Contents
- Acknowledgments
- Introduction
- 1 The Mathematical Minimum
- 2 Quantum Computing Fundamentals
- 3 Simulation Infrastructure
- 4 Quantum Tools and Techniques
- 5 Beyond Classical
- 6 Algorithms Exploiting Entanglement
- 7 State Similarity Tests
- 8 Black-Box Algorithms
- 9 State Preparation
- 10 Algorithms Using Amplitude Amplification
- 11 Algorithms Using Quantum Fourier Transform
- 12 Quantum Walk Algorithms
- 13 Optimization Algorithms
- 14 Quantum Machine Learning
- 15 Quantum Error Correction
- 16 Quantum Languages, Compilers, and Tools
- Appendix Sparse Implementation
- References
- Index
Summary
Quantum machine learning is an exciting field that explores the intersection of quantum computing and machine learning. It aims to leverage the principles of quantum computing to enhance machine learning algorithms and potentially revolutionize how we analyze data and solve complex problems. In this section, we begin with a simple algorithm for computing the Euclidean distance between vectors. Then we discuss the quantum principal component analysis. Finally, we explain the complex but beautiful HHL algorithm for solving systems of linear equations
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- Chapter
- Information
- Quantum Computing for Programmers , pp. 327 - 349Publisher: Cambridge University PressPrint publication year: 2025
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