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
Check out the course structure below for more information.
