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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

Appendix C
Computational Complexity

An algorithm is a finite answer to an infinite number of questions
— Stephen Kleene

Computational complexity theory is the branch of theoretical computer science that is concerned with quantifying the resources needed to solve problems with algorithms. It asks questions such as “How much time is needed to multiply two integer numbers of bits each?”, “Do you need more memory space to solve a problem than to check its solution?”, or “Is randomness useful in computational tasks?”.

In this brief introduction to computational complexity, we will focus mainly on the concepts involved in estimating how much time is required to solve certain problems. For a thorough treatment of this and other topics (including space or memory complexity, the role of randomness in computation, approximation algorithms, and other advanced matters), you can check standard computational complexity books such as the ones by Sipser...

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