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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

Product type Paperback

Published in Mar 2023

Publisher Packt

ISBN-13 9781804613832

Length 680 pages

Edition 1st Edition

Tools

Pennylane

Concepts

Machine Learning

Authors (2):

Elías F. Combarro Fernández-Combarro Álvarez

Samuel González Castillo

View More author details

Table of Contents (27) Chapters

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

Part I
I, for One, Welcome our New Quantum Overlords

This part introduces the main concepts behind the quantum circuit model. You will learn how qubits store information, how to operate on that information with quantum gates, and how to obtain results with quantum measurements. You will also learn about some of the most important tools currently used to program quantum computers. In particular, we will discuss how to implement and execute quantum circuits with Qiskit and PennyLane.

This part includes the following chapters:

Chapter 1, Foundations of Quantum Computing
Chapter 2, The Tools of the Trade in Quantum Computing

The rest of the chapter is locked

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Authors (2)

Elías F. Combarro Fernández-Combarro Álvarez

Elías F. Combarro Fernández-Combarro Álvarez

Elías F. Combarro holds degrees from the University of Oviedo (Spain) in both Mathematics (1997, award for second highest grades in the country) and Computer Science (2002, award for highest grades in the country). After some research stays at the Novosibirsk State University (Russia), he obtained a Ph.D. in Mathematics (Oviedo, 2001) with a dissertation on the properties of some computable predicates under the supervision of Prof. Andrey Morozov and Prof. Consuelo Martínez. Since 2009, Elías F. Combarro has been an associate professor at the Computer Science Department of the University of Oviedo. He has published more than 50 research papers in international journals on topics such as Computability Theory, Machine Learning, Fuzzy Measures and Computational Algebra. His current research focuses on the application Quantum Computing to algebraic, optimisation and machine learning problems. From July 2020 to January 2021, he was a Cooperation Associate at CERN openlab. Currently, he is the Spain representative in the Advisory Board of CERN Quantum Technology Initiative, a member of the Advisory Board of SheQuantum and one of the founders of the QSpain, a quantum computing think tank based in Spain.

See other products by Elías F. Combarro Fernández-Combarro Álvarez

Samuel González Castillo

Samuel González Castillo

Samuel González-Castillo holds degrees from the University of Oviedo (Spain) in both Mathematics and Physics (2021). He is currently a mathematics research student at the National University of Ireland, Maynooth, where he works as a graduate teaching assistant. He completed his physics bachelor thesis under the supervision of Prof. Elías F. Combarro and Prof. Ignacio F. Rúa (University of Oviedo), and Dr. Sofia Vallecorsa (CERN). In it, he worked alongside other researchers from ETH Zürich on the application of Quantum Machine Learning to classification problems in High Energy Physis. In 2021, he was a summer student at CERN developing a benchmarking framework for quantum simulators. He has contributed to several conferences on quantum computing.

See other products by Samuel González Castillo

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