This course refers to the use of quantum computing and quantum communication for solving computational problems in AI.
Unlike classical computation, quantum computing does not follow binary information processing, but quantum mechanical principles of superposition, interference, and entanglement of quantum states of particles. Though there is no strictly common concensus on the interpretation of quantum mechanics for our conception of reality, there is on its mathematical description and use for computation.
This course on QAI takes the computer science perspective and is structured into two parts: The first part briefly introduces the basic concepts of quantum computation with quantum bits in the gate-based and adiabatic quantum computational models and illustrates these concepts with a few prominent basic quantum algorithms. In the second part, we look at the feasability and potential benefit of leveraging quantum comnputational means for solving selected problems of AI, with focus on machine learning, and optimisation illustrated by selected quantum AI algorithms with applications in the real world.
This advanced course aims at advanced master students in Computer Science who preferably hold a B.Sc. degree in this or related field. Good knowledge of AI (introductory course on AI), machine learning, and mathematics - in particular linear algebra - is required. Selected background references are given on the lectures page.
The course will be held together with the DFKI.
Please refer to their webpage for further information.