Student Assistant - Quantum Machine Learning / Quantum Circuit Compilation

The »Self-Learning Systems« group at Fraunhofer IIS is part of the »Precise Positioning and Analytics« department and located at the institute’s Nuremberg site. Our team members have diverse academic backgrounds from computer science, engineering and physics.  One of our research goals is to provide robust and safe algorithmic solutions for control and decision-making tasks in complex dynamic environments. To this end, we employ a range of approaches from machine learning and deep learning in combination with reinforcement-learning techniques. This allows us to generate adaptive decision-making policies capable of processing high-dimensional state representations, such as camera images and other types of sensor readings.

»Quantum Machine Learning« is an emerging research field at the cross section of artificial intelligence and quantum computation. Spurred by the recent experimental demonstration of so-called quantum advantage, the research community has increased its efforts to propose new types of quantum algorithms for the still imperfect quantum computing devices we have today. In the »Self-Learning Systems« group, we want to explore the overlap of quantum computing and machine learning. On the one hand, adaptive quantum algorithms (so-called variational quantum circuits), can be integrated into our machine learning and reinforcement-learning based solutions. On the other hand, machine learning can assist the implementation of algorithms. »Quantum circuit compilation« is a challenging task given the NISQ (noisy intermediate scale quantum) era quantum computers of today.

Would you like to expand your programming skills and learn about interesting new topics in the field of quantum machine learning?

Then have a look at our offer!