Artificial Intelligence for the analytics of quantum devices
Unsupervised machine learning (ML), and in particular data clustering, is a powerful ap-proach for the analysis of datasets and identification of characteristic features occurring throughout a dataset. It is gaining popularity across scientific disciplines and is particular-ly useful for applications without a priori knowledge of the data structure. In our lab, we have developed several ML approaches for the classification of univariate measurements and apply it to the field of nanoelectronics and spectroscopy. This allows us to identify meaningful structures in data sets, providing physically relevant information about the system under study.
- Multiple machine learning methods (data classification, GANs, convolutional neu-ral networks, …).
- Physics of nanoscale quantum devices.
- Measurements on quantum devices.
- High-performance computing.
Prof. Dr. Mickael Lucien Perrin
Group Leader Quantum Devices
Deputy Head of Laboratory
Phone: +41 58 765 4610