Quantum machine learning (QML) — the use of quantum computers to solve machine learning problems — is not only an exciting topic. It is also an excellent basis for science fiction. But, unfortunately, science fiction is a double-edged sword.
First, there is the scientific part. In quantum machine learning, everything scientific is full of physics jargon and mathematical equations. Few students have the perseverance to work through the bone-dry material. And even fewer teachers seem to try to teach the subject in a halfway interesting way, let alone understandable.
That’s why I’ve made it my mission to explain quantum computing and quantum machine learning in an accessible way. In a way that developers, programmers, and interested students of any discipline with at least some programming experience can understand.
I genuinely believe developers, programmers, and students with at least some programming experience can become proficient in quantum machine learning. However, teaching quantum machine learning the right way requires a different approach — a hands-on approach.
This is the approach of Hands-On Quantum Machine Learning With Python.
In the first volume, “Getting Started,” you will not only implement different quantum machine learning algorithms, such as Quantum Naïve Bayes and Quantum Bayesian Networks. But you will learn to use them to solve problems taken from Kaggle.
In the second volume, “Combinatorial Optimization,” you will learn how to solve current optimization problems on real quantum computers. We will dive deep into the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimization Algorithm (QAOA) and use them to solve combinatorial optimization problems.
Hands-On Quantum Machine Learning With Python strives to be the perfect balance between the theory taught in a textbook and the actual hands-on knowledge you’ll need to implement real-world solutions.
Do you want to get started with Quantum Machine Learning? Have a look at Hands-On Quantum Machine Learning With Python.
Get the first three chapters for free.