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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 Part I: I, for One, Welcome our New Quantum Overlords
Chapter 1: Foundations of Quantum Computing Chapter 2: The Tools of the Trade in Quantum Computing Part II: When Time is Gold: Tools for Quantum Optimization
Chapter 3: Working with Quadratic Unconstrained Binary Optimization Problems Chapter 4: Adiabatic Quantum Computing and Quantum Annealing Chapter 5: QAOA: Quantum Approximate Optimization Algorithm Chapter 6: GAS: Grover Adaptive Search Chapter 7: VQE: Variational Quantum Eigensolver Part III: A Match Made in Heaven: Quantum Machine Learning
Chapter 8: What Is Quantum Machine Learning? Chapter 9: Quantum Support Vector Machines Chapter 10: Quantum Neural Networks Chapter 11: The Best of Both Worlds: Hybrid Architectures Chapter 12: Quantum Generative Adversarial Networks Part IV: Afterword and Appendices
Chapter 13: Afterword: The Future of Quantum Computing
Assessments Bibliography
Index
Other Books You May Enjoy Appendix A: Complex Numbers
Appendix B: Basic Linear Algebra Appendix C: Computational Complexity Appendix D: Installing the Tools Appendix E: Production Notes

Summary

In this chapter, you have learned about QAOA, one of the most popular quantum algorithms used to solve optimization problems with gate-based quantum computers. You now know that QAOA is derived as a discretization of quantum annealing and that it is implemented as a hybrid method that uses both a classical and a quantum computer to achieve its goal.

You also understand how to construct circuits for all the operations needed in the quantum part of the algorithm. In particular, you know how to use these circuits to estimate expectation values in an efficient way.

You have also mastered the tools that Qiskit provides in order to implement QAOA instances and to run them on both quantum simulators and quantum computers. You even know how to accelerate the process of running your code on quantum devices by using Qiskit Runtime. And, should you need to use QAOA with PennyLane, you also know how to do it with the help of some predefined utilities and PennyLane capabilities for automatic...

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