Hey everyone, my name is Tobias Fellner and I am starting as a doctoral student at the ICP. After finishing my master's thesis at the institute, where I investigated quantum machine learning for time series prediction tasks, I will continue working in this exciting field.
The motivation behind quantum machine learning lies in its potential to exploit the unique properties of quantum mechanics, such as superposition and entanglement. While several branches of quantum machine learning have emerged, I am particularly interested in quantum reservoir computing. In this paradigm, temporal data is processed by the dynamics of a quantum system. Observables of the quantum system are measured and only a classical neural network, as simple as a single layer, is trained on the measurement results to perform some learning task.
Throughout my Ph.D., I plan to investigate quantum mechanical properties of quantum reservoir computing, such as the degree of entanglement and the occupied phase space dimension, and relate these to model characteristics, such as generalization, expressiveness, and robustness. By investigating these properties, my research aims to advance our understanding of quantum reservoir computing, potentially leading to the identification of practical applications for currently available quantum computers.