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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

7.1 Hamiltonians, observables, and their expectation values

So far, we’ve found in Hamiltonians a way to encode combinatorial optimization problems. As you surely remember, in these optimization problems, we start with a function that assigns real numbers to binary strings of a certain length , and we seek to find a binary string with minimum cost . In order to do that with quantum algorithms, we define a Hamiltonian such that

\left\langle x \right|H_{f}\left| x \right\rangle = f(x)

holds for every binary string of length . Then, we can solve our original problem by finding a ground state of (that is, a state such that the expectation value is minimum).

This was just a very quick summary of Chapter 3, Working with Quadratic Unconstrained Binary Optimization Problems. When you read that chapter, you may have noticed that the Hamiltonian associated to has an additional, very remarkable property. We have mentioned this a couple of times already, but it is worth remembering that, for every computational basis state , it holds...

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