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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

3.4 Combinatorial optimization problems with the QUBO model

In this final section of the chapter, we are going to introduce some techniques that will allow us to write many important optimization problems as QUBO and Ising instances, so we can later solve them with different quantum algorithms. These examples will also help you understand how to formulate your own optimization problems under these models, which is the first step in order to be able to use quantum computers to solve them.

3.4.1 Binary linear programming

Binary linear programming problems involve optimizing a linear function on binary variables subject to linear constraints. Thus, the general form is

where are integer coefficients, is an integer matrix, is the transpose of , and is an integer column vector.

An example of this type of problem could be the following:

Binary linear programming (also known as zero-one linear programming) is -hard. In fact, the decision version in which the goal is to determine if there...

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