Annotation
This training course on the basics of Quantum Machine Learning (QML) is intended to explore the intersection of quantum computing and machine learning. During the event, the unique advantages and potential applications of combining these two cutting-edge fields will be highlighted. It will build on a foundational understanding of quantum computing and variational quantum algorithms. The course will follow this up with an explanation of parameterised quantum circuits and their use in quantum classification and regression. Then, attention will be focused on the quantum kernels of the support vector machine method. The course will conclude with an explanation of the quantum version of unsupervised machine learning. Each section will involve practical hands-on exercises to better understand the methods discussed.
Target Audience and Purpose of the Course:
Participants will gain an awareness of the principles of the basic types of QML methods. They will learn to implement QML models such as parameterised quantum circuits and quantum support vector machines, understanding the nuances of quantum algorithms and their applications in machine learning tasks like classification, regression, and clustering.
Level
Intermediate
Language
English
Prerequisites
Linear algebra, Python, Basics of quantum computing, variational quantum algorithms, and machine learning methods.
