5 papers
Quantum Boltzmann Machines for Sample-Efficient Reinforcement Learning
Thore Gerlach, Michael Schenk, Verena Kain
We introduce theoretically grounded Continuous Semi-Quantum Boltzmann Machines (CSQBMs) that supports continuous-action reinforcement learning. By combining exponential-family prio…
Geoff: The Generic Optimization Framework & Frontend for Particle Accelerator Controls
Penelope Madysa, Sabrina Appel, Verena Kain +1
Geoff is a collection of Python packages that form a framework for automation of particle accelerator controls. With particle accelerator laboratories around the world researching…
Towards Unlocking Insights from Logbooks Using AI
Antonin Sulc, Alex Bien, Annika Eichler +15
Electronic logbooks contain valuable information about activities and events concerning their associated particle accelerator facilities. However, the highly technical nature of lo…
Bayesian Optimization Algorithms for Accelerator Physics
Ryan Roussel, Auralee L. Edelen, Tobias Boltz +23
Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simul…
Progress in End-to-End Optimization of Detectors for Fundamental Physics with Differentiable Programming
Max Aehle, Lorenzo Arsini, R. Belén Barreiro +27
In this article we examine recent developments in the research area concerning the creation of end-to-end models for the complete optimization of measuring instruments. The models…