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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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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

Summary

In this chapter, we have introduced the quantum circuit model and the main concepts that it relies on: qubits, gates, and measurements. We have started by studying the most humble circuits, those that only have one or two qubits, but we have used our experience with them to grow all the way up to multi-qubit systems. In the process, we have discovered some powerful properties, such as superposition and entanglement, and we have mastered the mathematics — mainly some linear algebra — needed to work with them.

These notions will be extremely valuable to us, because they make up the language in which we will be describing the quantum algorithms for machine learning and optimization that we will study in the rest of the book. Soon, all the pieces will come together to form a beautiful structure. And we will be able to appreciate it and understand it fully because of the solid foundations that we have acquired by now.

In the next chapter, we will start applying all that we have learned by implementing and running quantum circuits on quantum simulators and on actual quantum computers. We don't know about you, but we are pretty excited!

You have been reading a chapter from
A Practical Guide to Quantum Machine Learning and Quantum Optimization
Published in: Mar 2023
Publisher: Packt
ISBN-13: 9781804613832
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