3 citations · 3 across the 4 of their papers we have counts for
4 papers
Transformer-based machine learning using low-level calorimeter signals for collimated photon identification at collider experiments
Gabriel Matos, Lauren Larson, Abhilasha Dave +8
Electromagnetic calorimeters provide essential information for reconstructing and selecting both Standard Model (SM) and potential beyond the SM physics events at high-energy parti…
Reinforcement Learning for Adaptive Composition of Quantum Circuit Optimisation Passes
Daniel Mills, Ifan Williams, Jacob Swain +3
Many quantum software development kits provide a suite of circuit optimisation passes. These passes have been highly optimised and tested in isolation. However, the order in which…
You Only Measure Once: On Designing Single-Shot Quantum Machine Learning Models
Chen-Yu Liu, Leonardo Placidi, Kuan-Cheng Chen +2
Quantum machine learning (QML) models conventionally rely on repeated measurements (shots) of observables to obtain reliable predictions. This dependence on large shot budgets lead…
Semi-supervised permutation invariant particle-level anomaly detection
Gabriel Matos, Elena Busch, Ki Ryeong Park +1
The development of analysis methods to distinguish potential beyond the Standard Model phenomena in a model-agnostic way can significantly enhance the discovery reach in collider e…