Quantum Machine Learning (with IBM Quantum Research) (openHPI)

This course is aimed at students, experts and enthusiasts of quantum computing or machine learning. Prior knowledge about quantum computing or quantum information are strongly recommended.

Machine Learning has revolutionized our lives: image classification, natural language processing, drug discovery, weather forecasting, predictive maintenance, etc. The list of applications grows continuously. All of these models rely on the availability of powerful computers. In fact, over the past decades the computational resources of one chip have doubled every year. Currently, however, we are approaching the physical limitations of what classical computers can achieve. Yet our resource requirements keep increasing! Research institutes and industry are, thus, looking into alternative computing models such as quantum computing. With this emerging technology we may be able to push computational applications even further and tackle new challenges that are currently out of reach for existing classical processors.

As an interdisciplinary topic, this course is aimed at a broad audience. Students, experts, professionals and enthusiast from the fields of quantum computing, machine learning, physics and computer science are welcome to enroll!

In this course we will:

- understand how to build both basic and advanced quantum machine learning models

- implement classical and quantum algorithms to solve machine learning tasks with Python and Qiskit

- learn about roadblocks and challenges of quantum machine learning

- explore the future prospects of quantum machine learning

What you'll learn

- What is quantum machine learning?

- What are quantum machine learning algorithms?

- What are suitable applications of quantum machine learning?

- How can I implement quantum machine learning algorithms?

- What are potentials and bottlenecks of quantum machine learning?

Course contents

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!