Quantum Machine Learning (with IBM Quantum)Dr. Christa Zoufal, Julien Gacon, Dr. David Sutter

The listed learning units belong to the course Quantum Machine Learning (with IBM Quantum). Do you want to access all course content?

Intro

This course aims at enabling you to discover the field of Quantum Machine Learning. You will learn about the basics, such as parameterized quantum models and training algorithms, investigate promising models which are compatible with today's quantum hardware, and learn how to write Quantum Machine Learning algorithms by yourself with Qiskit.

Week 1

After giving an overview answering the question: "What is Quantum Machine Learning?", we will present a general introduction to machine learning followed by a deep-dive into Support Vector Machines and their quantum counter-part Quantum Support Vector Machines. Finally, we present a variational Quantum Machine Learning classification algorithm called the Variational Quantum Classifier.

Week 2

In the second week of the course, we will firstly discuss how Quantum Machine Learning models are being trained. Then, we have a closer look at two specific models, i.e., Quantum Generative Adversarial Networks and Quantum Boltzmann machines. Furthermore, we give a practical coding introduction to Machine Learning with Qiskit. Lastly, we explore the potential of Quantum Machine Learning in a discussion with an expert in the field.

Final exam

We hope you enjoyed this course on Quantum Machine Learning and wish you good luck for the final exam!