Speaker
Description
At the High Luminosity Large Hadron Collider (HL-LHC) and beyond, we will enter the “exa-byte” era, where the annual computing cost will increase by a factor of 10-20 from the ongoing LHC program. Without various innovations, future experiments will not be able to operate. The Graphical Processing Units (GPU) and other state-of-the-art artificial intelligence technologies will be the baseline at the HL-LHC. Quantum computing may also bring another “leap”. Two of the highly CPU consuming components are the track reconstruction in both data/simulation and simulation of shower development in the calorimeter. Tackling these challenges will be useful for future colliders, such as the HL-LHC, Circular Electron Positron Collider (CEPC) and Super proton-proton Collider (SppC). In this talk, I will present recent studies on the former topic, namely on an application of quantum machine learning for the track reconstruction.
