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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

10.1 Building and training a quantum neural network

Just like quantum support vector machines, quantum neural networks are what we called ”CQ models” back in Chapter 8, What is Quantum Machine Learning?, — models with purely classical inputs and outputs that use quantum computing at some stage. However, unlike QSVMs, quantum neural networks are not a ”particular case” of any classical model, although their behavior is inspired by that of classical neural networks. What is more, as we will soon see, quantum neural networks are ”purely quantum” models, in the sense that their execution will only require classical computing for the preparation of circuits and the statistical analysis of measurements. Nevertheless, just like QSVMs, quantum neural networks will depend on classical parameters that will be optimized classically.

To learn more

As you surely know by now, (quantum) machine learning is a vast field in which terms hardly...

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