Hands-On Quantum Machine Learning With Python
Welcome to Hands-On Quantum Machine Learning With Python. This Weekly Column is your comprehensive guide to get started with "Quantum Machine Learning" - the use of quantum computing for computation of machine learning algorithms.
Hands-On Quantum Machine Learning With Python strives to be the perfect balance between theory taught in a textbook and the actual hands-on knowledge you’ll need to implement real-world solutions.
This weekly column covers the basics of quantum computing and machine learning in a practical and applied manner. You will learn to use state-of-the-art quantum machine learning algorithms, too.
With this weekly column, you will be in the pole position to become a "Quantum Machine Learning Engineer" - the job to become the sexiest job of the 2020s.
What is this about?
Quantum computing promises to make it possible to solve problems intractable with current computing technologies. But is it fundamentally different and asks us to change the way we think.
This weekly column provides a no-nonsense teaching style guaranteed to cut through all the cruft and help you master Quantum Machine Learning (QML)
Hands-on tutorials (with lots of code) that not only show you the concepts of quantum computing and the algorithms behind machine learning but their implementations as well.
You're probably wondering... “Is this column right for me?”
I write this weekly column for you - for developers, programmers, students, and researchers who have at least some programming experience and want to become proficient in Quantum Machine Learning.
We will go through the basics of quantum computing. But you won't need to be a quantum physician to follow.
We will use machine learning algorithms and libraries. But you don't need to be an experienced data scientist.
We will use Python and write a lot of code. But you won't need to be a senior developer.
Why should I care about Quantum Machine Learning?
In the recent past, we have witnessed how algorithms learned to drive cars and beat world champions in chess and Go. Machine learning is being applied to virtually every imaginable sector, from military to aerospace, from agriculture to manufacturing, and from finance to healthcare.
But these algorithms become increasingly hard to train because they consist of billions of parameters. Quantum computers promise to solve such problems intractable with current computing technologies. Their ability to compute multiple states simultaneously enables them to perform an indefinite number of superposed tasks in parallel. An ability that promises to improve and to expedite machine learning techniques.
Unlike classical computers that are based on sequential information processing, quantum computing makes use of the properties of quantum physics. Superposition, entanglement, and interference. But rather than increasing the available computing capacity, it reduces the complexity of a problem. It makes the problem tractable.
But quantum computing requires us to change the way we think about computers. It requires a whole new set of algorithms. Algorithms that encode and use quantum information. This includes machine learning algorithms.
And it requires a new set of developers. Developers who understand machine learning and quantum computing. Developers capable to solve practical problems that have not been solved before. A rare type of developer. The ability to solve quantum machine learning problems today already sets you apart from all the others.
Quantum machine learning promises to be disruptive. Although this merger of machine learning and quantum computing, both areas of active research, is largely in the conceptual domain, there are already a number of examples where it is being applied to solve real-life problems. Google, Amazon, IBM, Microsoft, and a whole fleet of high-tech startups strive to be the first to build and sell quantum machine learning systems.
The opportunity to study a technology right at the moment when it is about to prove its supremacy is a unique opportunity. Don't miss it.