Quantum machine learning
The main aim of quantum machine learning is to find efficient quantum algorithms for the most challenging quantum machine learning problems. In this context, unsupervised problems play an important role since they are mostly unsolved despite the recent success of deep learning. Broader, quantum machine learning explores the connections between machine learning and quantum physics. For instance, two crucial research directions of quantum machine learning are the application of quantum-mechanics tools to machine learning and the application of machine-learning tools to describe entangled quantum states. These directions are essential to understand the boundaries of classical computation and the benefits of quantum processing. In the course, we will present connections between quantum mechanics and machine learning that are important for the application of noisy intermediate-scale quantum devices to machine learning problems. The course is hands-on. We will provide multiple notebooks to guide the students during the learning process and acquaint them with available quantum computational tools. Part of the course will also be " 2 / 5 devoted to the most prominent algorithms for universal quantum computers. In the last two weeks, we will explore recent, more theoretical connections between machine learning and quantum mechanics.
