[ONLINE] Basics of Quantum Machine Learning

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.

Tutor

Jiří Tomčala is a researcher in the Quantum Computing laboratory of the IT4Innovations National Supercomputer Center. He graduated with a degree in Applied Mathematics in 2016 and earned his Ph.D. in Computer Science in 2021. Between 2021 and 2025, he received 5 Certificates of Quantum Excellence and 1 Quantum Challenge achievement from IBM Quantum. He is an active publishing researcher in the field of quantum computing and regularly contributes his latest results to scientific conferences and journals. His current research interest lies in the use of quantum computation to solve optimisation problems, factorisation, and also in quantum machine learning, especially in hybrid quantum neural networks. Thus, he focuses on variational quantum algorithms, the composition of variational quantum circuits and on the specific applications of Shor's and Grover's algorithms.

Acknowledgements

This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 101101903. The JU receives support from the Digital Europe Programme and Germany, Bulgaria, Austria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, Greece, Hungary, Ireland, Italy, Lithuania, Latvia, Poland, Portugal, Romania, Slovenia, Spain, Sweden, France, Netherlands, Belgium, Luxembourg, Slovakia, Norway, Türkiye, Republic of North Macedonia, Iceland, Montenegro, Serbia. This project has received funding from the Ministry of Education, Youth and Sports of the Czech Republic.

This course was supported by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90254).

All presentations and educational materials of this course are provided under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.