Feature Reduction Improves Quantum Extreme Learning Machines

Annalisa De Lorenzis detailed a new approach to machine learning in her thesis, bridging quantum computing and the analysis of data from water Cherenkov detectors used in neutrino physics. Her thesis explores Quantum Extreme Learning Machines, or QELMs, a hybrid framework encoding classical data into quantum states before processing them with a classical readout layer. Within this system, De Lorenzis analyzed the interplay between quantum dynamics, expressivity, and entanglement, and how easily these quantum processes can be replicated by conventional computers. The work also demonstrates the successful application of convolutional architectures, including residual networks, for classifying complex events within simulated neutrino detector data, highlighting the potential of machine learning, in both quantum and classical forms,…