Hands-On Quantum Machine Learning With Python: Volume 2: Combinatorial Optimization - Softcover

Zickert, Dr. Frank

Softcover

ISBN 13: 9798374278491

Publisher: Independently published, 2023

Synopsis

Do you want to become a quantum machine learning practitioner?
... But you don't want to study theoretical physics first
Then, "Hands-On Quantum Machine Learning With Python" is for you.

This book has one goal — to help developers, practitioners, and students like yourself become quantum machine learning experts.

It doesn't matter if it is the first time you have worked with machine learning and quantum computing. Hands-On Quantum Machine Learning With Python is engineered from the ground up to help you reach expert status.

Inside this book, you'll find:

• Super practical walkthroughs present solutions to real-world combinatorial optimization problems and challenges.
• Hands-on tutorials (with lots of code) show you the Variational Quantum Eigensolver and its implementation and usage.
• An accessible teaching style guaranteed to get you through the underlying maths and physics and master machine quantum learning.

In this volume, you will learn how to solve current optimization problems on real quantum computers. We will dive deep into the Variational Quantum Eigensolver (VQE) and use it to solve combinatorial optimization problems.

Combinatorial optimization is of paramount importance in many industries. For example, the famous Traveling Salesman Problem (TSP) asks for the shortest route between different cities. It is crucial for parcel delivery, aviation, and almost all mobility-related fields.

The ability to solve these problems will enable you to be well prepared to find or keep a job in any of these fields being disrupted by the advent of quantum computing.

-- Is this book right for me? --

You don't need to be a mathematician.

You don't need to be a physicist, either.

This book is for students, developers, data scientists, and practitioners interested in applying quantum machine learning to actual problems - today.

"I am new to quantum computing and machine learning altogether." - No problem! Hands-On Quantum Machine Learning With Python is precisely what you need. We start with the absolute basics. We assume no prior knowledge of machine learning or quantum computing. You will not be left behind. (Please claim a bundle including "Volume 1: Getting Started").

"I have a computer science or programming background. Will I understand quantum machine learning?" - Absolutely! This book explains quantum machine learning in an accessible way, even if you are not a mathematician or a physicist. You'll find many code examples and explanations in no other book!

"I'm an experienced data scientist or machine learning engineer." - The problems we solve will be familiar to you, but how we solve them will be new. The quantum algorithms we use will become an entirely new tool in your toolbox that you may not have even known existed. And yet, it's the tool you need to master if you want to keep your job in the future.

"I am an expert in my field. But I don't have a Ph.D. What are my chances of becoming an expert in quantum computing?" Employers are looking for a rare mix of skills. On the one hand, they look for candidates who are experts in their field. On the other hand, they are looking for candidates with a well-equipped toolbox for machine learning with quantum computing. You are in pole position!

-- What's inside this book? --

Hands-On Quantum Machine Learning With Python will make you an expert in solving combinatorial optimization problems with a quantum computer.

Inside the book, we will focus on the following:

• Combinatorial optimization
• The Variational Quantum Eigensolver (VQE)
• Problem formulation
• Various solution ansatzes
• Running algorithms on real quantum computers
• Quantum error mitigation
• The Quantum Approximate Optimization Algorithm

Edition details

• Publisher: Independently published
• Publication date: 2023
• ISBN 13: 9798374278491
• Binding: Paperback
• Language: English
• Number of pages: 434