A Practical Guide to Quantum Machine Learning and Quantum Optimization

Table of Contents

1. Foundations of Quantum Computing
2. The Tools of the Trade in Quantum Computing
3. Working with Quadratic Unconstrained Binary Optimization Problems
4. Adiabatic Quantum Computing and Quantum Annealing
5. QAOA: Quantum Approximate Optimization Algorithm
6. GAS: Grover Adaptative Search
7. VQE: Variational Quantum Solver
8. What is Quantum Machine Learning?
9. Quantum Support Vector Machines
10. Quantum Neural Networks
11. The Best of Both Worlds: Hybrid Architectures
12. Quantum Generative Adversarial Networks
13. Afterword: The Future of Quantum Computing
14. Complex Numbers
15. Basic Linear Algebra
16. Computational Complexity
17. Installing the Tools
18. Production Notes