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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

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

3-CNF 284

A

activation function 472

Adam algorithm 489

adiabatic evolution 207

adiabatic quantum computing 7, 206, 207, 208, 209

discretizing 255, 257, 259

adiabatic theorem 208

adjacent vertices 189

adjoint transpose 15

for PennyLane device 121

Amazon Braket 84

amplitude amplification 331

amplitude encoding 559, 560

amplitudes 13

Anaconda 813

Distribution version 814

installation 814

data output 583

data preparation 582

data processing 583

AND gate 11

angle encoding 555, 559

anneal fraction 241

annealer topologies 232, 235

controlling 239, 240, 240, 242, 243, 243, 244

annealing process 239

annihilation operator 420

ansatzs 406, 628

API token field 821

arithmetic modulo 4 776, 777

artificial feed-forward dense neural network 476

asymptotic complexity 784, 785, 789

attributes 450

AUC (area under ROC curve) 511

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