A Practical Guide to Quantum Machine Learning and Quantum Optimization
This book introduces the main quantum algorithms that are currently used in optimization and machine learning. The approach is hands-on, with examples that can be run on simulators and actual quantum computers. The algorithms are explained in full detail, without sacrificing rigor, but, at the same time, keeping mathematical prerequisites to a minimum.
Offered by
Difficulty Level
Intermediate
Completion Time
22h40m
Language
English
About Book
Who Is This Book For?
This book is for professionals from a wide variety of backgrounds, including computer scientists and programmers, engineers, physicists, chemists, and mathematicians. Basic knowledge of linear algebra and some programming skills (for instance, in Python) are assumed, although all mathematical prerequisites will be covered in the appendices.
A Practical Guide to Quantum Machine Learning and Quantum Optimization
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 22h40m total length
Foundations of Quantum Computing
The Tools of the Trade in Quantum Computing
Working with Quadratic Unconstrained Binary Optimization Problems
Adiabatic Quantum Computing and Quantum Annealing
QAOA: Quantum Approximate Optimization Algorithm
GAS: Grover Adaptative Search
VQE: Variational Quantum Solver
What is Quantum Machine Learning?
Quantum Support Vector Machines
Quantum Neural Networks
The Best of Both Worlds: Hybrid Architectures
Quantum Generative Adversarial Networks
Afterword: The Future of Quantum Computing
Complex Numbers
Basic Linear Algebra
Computational Complexity
Installing the Tools
Production Notes
Related Resources
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