Quantum Artificial Intelligence (QAI) refers to the use of quantum computing for solving computational problems in AI, and vice versa. 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 consensus on the interpretation of quantum mechanics for our conception of reality, there is one on the mathematical description and use of quantum mechanical principles 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 feasibility and potential benefit of leveraging quantum computational means for solving selected problems of AI, with focus on machine learning, and optimization illustrated by selected quantum AI algorithms with applications in the real world.
Course webpage: https://www.dfki.de/~klusch/QAI-course-ws25/