The term "Quantum Machine Learning" (QML) describes a dynamic field of research that combines approaches of machine learning and quantum information processing. The use of quantum computers has the potential to make machine learning methods more efficient and to tackle problems that were previously difficult to solve or even inaccessible. Although QML has no practical relevance at present, significant progress can be expected here in the coming years - especially due to the investments and developments in the area of quantum computing. Consequently, QML must already be subjected to an in-depth consideration now in order to enable a forward-looking discussion of potential opportunities and risks with regard to IT security.

The German Federal Office for Information Security (BSI) commissioned Capgemini and the Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS--Fraunhofer Institute for Intelligent Analysis and Information Systems) to prepare a study that comprehensively presents the current state of the art in QML research and examines it from the perspective of IT security. In the first step, the existing QML methods were categorised, set out and evaluated on the basis of algorithmic and hardware-related criteria with regard to their current and expected future practical suitability:

Quantum Machine Learning – State of The Art and Future Directions

The results of this first part are discussed further in the context of IT security in the final study. For this purpose, the security properties of the respective methods as well as potential applications of QML in various usage scenarios from IT security will be discussed. The corresponding findings will be published in the course of 2022.