4 papers
Reducing Simulation Dependence in Neutrino Telescopes with Masked Point Transformers
Felix J. Yu, Nicholas Kamp, Carlos A. Argüelles
Machine learning techniques in neutrino physics have traditionally relied on simulated data, which provides access to ground-truth labels. However, the accuracy of these simulation…
Qubit thermodynamics: Entropy production from nonadiabatic driving
Pavel Zhelnin, Lucas Johns, Carlos A. Argüelles
Adiabaticity is a cornerstone of many promising approaches to quantum control, computing, and simulation. In practice, however, there is always a trade-off. Although the deleteriou…
Neutrino Telescope Event Classification on Quantum Computers
Pablo Rodriguez-Grasa, Pavel Zhelnin, Carlos A. Argüelles +1
Quantum computers represent a new computational paradigm with steadily improving hardware capabilities. In this article, we present the first study exploring how current quantum co…
Learning Efficient Representations of Neutrino Telescope Events
Felix J. Yu, Nicholas Kamp, Carlos A. Argüelles
Neutrino telescopes detect rare interactions of particles produced in some of the most extreme environments in the Universe. This is accomplished by instrumenting a cubic-kilometer…