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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

4.3 Using Ocean to formulate and transform optimization problems

As we have just seen, the BinaryQuadraticModel class can be used to define both Ising and QUBO problems. But dimod also offers other models and utilities that will make our lives a little bit easier. Let’s start by studying how we can conveniently define problems with linear restrictions.

4.3.1 Constrained quadratic models in Ocean

You surely remember that a problem like

is an instance of binary linear programming. In Section 3.4.1, we studied this family of problems in detail and we showed that they can be transformed into the QUBO and Ising models by using slack variables and penalty terms.

So, imagine that you want to solve the preceding problem in a quantum annealer. Do you need to perform all those boring transformations in order to obtain the QUBO coefficients and then use them to define a BinaryQuadraticModel object? No! Fortunately, dimod provides the ConstrainedQuadraticModel class, which simplifies...

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