This course introduces fundamental concepts and practical techniques in quantum machine learning (QML), an interdisciplinary field bridging quantum computing and classical machine learning. By leveraging the computational advantages of quantum systems - including superior runtime efficiency and scalability - QML promises novel solutions that may surpass classical approaches. This course begins with an introduction to the basics of quantum computing and classical machine learning, then explores major quantum machine learning frameworks. Both the methodology description and the theoretical analysis are presented for a comprehensive understanding, reinforced through hands-on projects demonstrating practical applications.

The examination assesses students' mastery of fundamental knowledge about quantum computing and machine learning models and their underlying principles.