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SUMMARY:Machine Learning Development for Quantum Computing and Neutrino Ph
 ysics
DTSTART:20260710T090000Z
DTEND:20260710T120000Z
DTSTAMP:20260709T104800Z
UID:indico-event-2592@indico.ifae.es
DESCRIPTION:Speakers: Annalisa De Lorenzis\n\nAbstract: This thesis explo
 res how machine learning can build effective representations of complex ph
 ysics data. It studies Quantum Extreme Learning Machines as hybrid classic
 al-quantum machine learning frameworks using features for image classifica
 tion\, investigating the role of encoding\, dynamics\, entanglement and cl
 assical simulability. The thesis also applies deep learning to simulated W
 ater Cherenkov detector images\, using charge and timing information to di
 stinguish single-vertex neutrino events from pile-up events. Finally\, it 
 presents a first proof-of-concept application of QELMs to realistic neutri
 no detector images.\n \nSupervisors: M. Pilar Casado\, Arnau Riera i Thor
 sten Lux.\n \n\nhttps://indico.ifae.es/event/2592/
LOCATION:IFAE Seminar Room (IFAE Seminar Room)
URL:https://indico.ifae.es/event/2592/
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