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A Practical Guide to Quantum Machine Learning and Quantum Optimization

You're reading from   A Practical Guide to Quantum Machine Learning and Quantum Optimization Hands-on Approach to Modern Quantum Algorithms

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Product type Paperback
Published in Mar 2023
Publisher Packt
ISBN-13 9781804613832
Length 680 pages
Edition 1st Edition
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Authors (2):
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Elías F. Combarro Fernández-Combarro Álvarez Elías F. Combarro Fernández-Combarro Álvarez
Author Profile Icon Elías F. Combarro Fernández-Combarro Álvarez
Elías F. Combarro Fernández-Combarro Álvarez
Samuel González Castillo Samuel González Castillo
Author Profile Icon Samuel González Castillo
Samuel González Castillo
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Toc

Table of Contents (27) Chapters Close

Preface 1. Part I: I, for One, Welcome our New Quantum Overlords
2. Chapter 1: Foundations of Quantum Computing FREE CHAPTER 3. Chapter 2: The Tools of the Trade in Quantum Computing 4. Part II: When Time is Gold: Tools for Quantum Optimization
5. Chapter 3: Working with Quadratic Unconstrained Binary Optimization Problems 6. Chapter 4: Adiabatic Quantum Computing and Quantum Annealing 7. Chapter 5: QAOA: Quantum Approximate Optimization Algorithm 8. Chapter 6: GAS: Grover Adaptive Search 9. Chapter 7: VQE: Variational Quantum Eigensolver 10. Part III: A Match Made in Heaven: Quantum Machine Learning
11. Chapter 8: What Is Quantum Machine Learning? 12. Chapter 9: Quantum Support Vector Machines 13. Chapter 10: Quantum Neural Networks 14. Chapter 11: The Best of Both Worlds: Hybrid Architectures 15. Chapter 12: Quantum Generative Adversarial Networks 16. Part IV: Afterword and Appendices
17. Chapter 13: Afterword: The Future of Quantum Computing
18. Assessments 19. Bibliography
20. Index
21. Other Books You May Enjoy Appendix A: Complex Numbers
1. Appendix B: Basic Linear Algebra 2. Appendix C: Computational Complexity 3. Appendix D: Installing the Tools 4. Appendix E: Production Notes

10.1 Building and training a quantum neural network

Just like quantum support vector machines, quantum neural networks are what we called ”CQ models” back in Chapter 8, What is Quantum Machine Learning?, — models with purely classical inputs and outputs that use quantum computing at some stage. However, unlike QSVMs, quantum neural networks are not a ”particular case” of any classical model, although their behavior is inspired by that of classical neural networks. What is more, as we will soon see, quantum neural networks are ”purely quantum” models, in the sense that their execution will only require classical computing for the preparation of circuits and the statistical analysis of measurements. Nevertheless, just like QSVMs, quantum neural networks will depend on classical parameters that will be optimized classically.

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As you surely know by now, (quantum) machine learning is a vast field in which terms hardly...

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