6 citations · 6 across the 3 of their papers we have counts for
4 papers
Robust Quantum Machine Learning for Collider Event Selection under Detector Variability
Christopher Brown, Michael Spannowsky, Simon Williams
Robust machine-learning methods are becoming increasingly important for high-energy physics data analysis as experiments enter the era of higher luminosity and future higher-energy…
JetFormer: A Scalable and Efficient Transformer for Jet Tagging from Offline Analysis to FPGA Triggers
Ruoqing Zheng, Chang Sun, Qibin Liu +7
We present JetFormer, a versatile and scalable encoder-only Transformer architecture for particle jet tagging at the Large Hadron Collider (LHC). Unlike prior approaches that are o…
Advancing the CMS Level-1 Trigger: Jet Tagging with DeepSets at the HL-LHC
Stella Schaefer, Christopher Brown, Duc Hoang +2
At the High Luminosity LHC, selecting important physics processes such as (di-) Higgs production will be a high priority. The Phase-2 Upgrade of the CMS Level-1 Trigger will recons…
Quantum Pathways for Charged Track Finding in High-Energy Collisions
Christopher Brown, Michael Spannowsky, Alexander Tapper +2
In high-energy particle collisions, charged track finding is a complex yet crucial endeavour. We propose a quantum algorithm, specifically quantum template matching, to enhance the…