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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

Product type Book
Published in Mar 2023
Publisher Packt
ISBN-13 9781804613832
Pages 680 pages
Edition 1st Edition
Languages
Authors (2):
Elías F. Combarro Elías F. Combarro
Profile icon Elías F. Combarro
Samuel González-Castillo Samuel González-Castillo
Profile icon 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 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

Chapter 13
Afterword: The Future of Quantum Computing

I am not throwing away my shot!
— Alexander Hamilton

This has been a long and (hopefully) interesting journey. In the 12 chapters of this book, we’ve covered a lot of topics on quantum computing, both from a theoretical and a practical point of view, so maybe it’s time to take a look back and see what we have learned.

We started by laying the foundations. We studied the most important mathematical concepts underlying the theory of quantum computing, including how information is stored on qubits, how we can transform their states with quantum gates, and how we can obtain results by measuring them. Then, we explored some of the software tools currently available to implement quantum algorithms, with a special emphasis on the two main software libraries used in this book: Qiskit and PennyLane. We learned how to implement quantum circuits with both frameworks and how to run them on simulators and on actual quantum...

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A Practical Guide to Quantum Machine Learning and Quantum Optimization
Published in: Mar 2023 Publisher: Packt ISBN-13: 9781804613832
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