Jonas was working on "Investigating the influence of quantum effects in Quantum Reservoir Computing on its performance" supervised by Tobias Fellner. We congratulate him on this wonderful achievement and invite you to attend the defense.
Abstract: Quantum reservoir computing (QRC) is an emerging paradigm that integrates quantum mechanical principles into the field of reservoir computing to improve machine learning performance. Motivated by the potential computational advantages offered by intrinsic quantum properties such as entanglement, this thesis investigates the role of these properties in influencing the behavior of QRC architectures. In particular, we analyze the interplay between entanglement, covariance dimension, coupling strength, and prediction accuracy. Our results show a consistent trend: stronger coupling, which leads to increased entanglement, generally correlates with better performance. However, this improved performance diminishes as the complexity of the prediction task increases. In contrast, the covariance dimension shows limited correlation with performance, suggesting that existing measures may not fully capture the effective dimensionality of the system. Overall, our results highlight the importance of entanglement in QRC and suggest promising directions for further optimization. This work contributes to a deeper understanding of quantum machine learning and highlights the importance of exploiting quantum-specific features in the design of next-generation learning systems.
Best of luck Jonas on future endeavors!
