Tootfinder

The very high energy view of gamma-ray bursts with the MAGIC telescopes
Alessio Berti (on behalf of the MAGIC Collaboration), \v{Z}eljka Bo\v{s}njak (on behalf of the MAGIC Collaboration), Alberto Castro-Tirado (on behalf of the MAGIC Collaboration), Stefano Covino (on behalf of the MAGIC Collaboration), Susumu Inoue (on behalf of the MAGIC Collaboration), Francesco Longo (on behalf of the MAGIC Collaboration), Serena Loporchio (on behalf of the MAGIC Collaboration), Davide Miceli (on …

The very high energy view of gamma-ray bursts with the MAGIC telescopes
Gamma-ray bursts (GRBs) are one of the main targets for the observations of the MAGIC telescopes. As a result of the effort in improving the sensitivity of the instrument and the automatic follow-up strategy, MAGIC detected two GRBs in the very-high-energy (VHE, $E>100$ GeV) range, namely GRB 190114C and GRB 201216C. In GRB 190114C ($z=0.42$), the data collected by MAGIC revealed a new emission component at sub-TeV energies in the afterglow of the GRB. The very rich multi-wavelength dataset, sp…

Accelerating the drive towards energy-efficient generative AI with quantum computing algorithms
Frederik F. Fl\"other, Jan Mikolon, Maria Longobardi
https://arxiv.org/abs/2508.20720

Accelerating the drive towards energy-efficient generative AI with quantum computing algorithms
Research and usage of artificial intelligence, particularly generative and large language models, have rapidly progressed over the last years. This has, however, given rise to issues due to high energy consumption. While quantum computing is not (yet) mainstream, its intersection with machine learning is especially promising, and the technology could alleviate some of these energy challenges. In this perspective article, we break down the lifecycle stages of large language models and discuss re…