Quantum Computing and Quantum Machine Learning
The group’s main focus is on applications of machine learning (ML) to quantum physics. Topics that are addressed are related to quantum computing including quantum error correction using topological codes and variational quantum algorithms, as well as studying topological states of matter in condensed matter systems. Of particular interest is to utilize and adapt state-of-the-art deep learning algorithms to these types of problems. The work on quantum computing is done as part of the Wallenberg Centre for Quantum Technology (WACQT). As part of WACQT, we are also working on projects more closely related to the development of the actual quantum hardware with the aim of implementing near term error correction using small stabilizer codes.