Group research interests

Classical and Quantum Machine Learning

Transfer learning: predicting the anomalous exponent for experimental trajectories. Labels (i) and (ii) refer to different datasets analysed.

Our three main focus areas are:

i) developments of improved Monte Carlo and classical machine learning algorithms and applications for classical and quantum complex problems;

ii applications of classical machine learning to quantum many body physics;

iii) design and analysis of quantum neural networks; applications of machine learning to anomalous diffusion.