Hands-On Quantum Machine Learning With Python
It's getting real. Don't miss it!
Today, I want to use my weekly post for a personal message.
My book Hands-On Quantum Machine Learning With Python is almost finished!
Therefore, I feel confident to enter the next phase. I plan to start a Kickstarter campaign next Tuesday. Please mark April, 13th 2021 in your calendar and don't miss it.
During the past six months, I spent a considerable part of my free time writing the book. I published quite a few parts of the book on Medium. So, if you're not sure, please check them out. Further, you can get the first three chapters of the book for free on my homepage.
If you're still not sure whether the book is for you, write me a message. I try to be there for you.
Hands-On Quantum Machine Learning With Python has one goal. It aims to get you started with quantum machine learning in an easy-to-understand and comprehensive way.
The final book will have somewhere between 400 and 500 pages. I will publish it as an eBook and as a printed book. While the eBook will ship in June 2021, the printed book will take a little longer. My goal is July 2021.
We’re living at a time when knowledge and education are not limited to a small group of privileged persons. You can grab the latest research in quantum machine learning off the internet. There are plenty of scientific articles on Arxiv. There are a lot of books on machine learning and some on quantum computing, too. And, there are myriads of blog posts.
The problem is the literature on quantum computing is full of physical jargon and mathematical formulae. Pretty soon, you might get the feeling the topic is restricted to mathematicians and physicists holding a Ph.D.
Let’s take this quote, for instance:
VQE can help us to estimate the energy of the ground state of a given quantum mechanical system. This is the upper bound of the lowest eigenvalue of a given Hamiltonian. It builds upon the variational principle that is described as:
⟨ψλ|H|ψλ⟩>=E0
The first and natural reaction — if you don’t hold a degree in physics — is to put the article away.
“Well, nice try. Maybe the whole topic is not for me”, you think. “Maybe, quantum machine learning is beyond my reach”.
Don’t give up that fast. Most of the stuff in quantum computing was discovered by physicists and mathematicians. Of course, they build upon the knowledge of their peers when they share their insights and teach their students. It is reasonable they use the terms they are familiar with.
You wouldn’t use the vocabulary of a bartender to explain programming and machine learning either, would you?
It is reasonable to assume a certain kind of knowledge when we talk or write about something. But should we restrain students of other, nearby disciplines from learning the stuff? Why shouldn’t we support a computer scientist or a software engineer in learning quantum machine learning?
I’ve got a clear opinion. I believe anyone sincerely interested in quantum machine learning should be able to learn it. There should be resources out there catering to the needs of the student, not to the convenience of the teacher. Of course, this requires a teacher able to explain the complex stuff in allegedly simple language.
This is the goal of Hands-On Quantum Machine Learning with Python.
If you can’t explain it simply, you don’t understand it well enough.” — Albert Einstein
Whether you just get started with quantum computing and machine learning or you're already a senior machine learning engineer, Hands-On Quantum Machine Learning With Python is your comprehensive guide to get started with Quantum Machine Learning - the use of quantum computing for the computation of machine learning algorithms.
Inside Hands-On Quantum Machine Learning With Python, you'll learn the basics of machine learning and quantum computing.
You'll learn how to create parameterized quantum circuits and variational hybrid quantum-classical algorithms that solve classification tasks.
Learn about quantum superposition, entanglement, and interference and how you can use it to solve problems intractable for classical computers.
See you on April, 13th 2021!
