Qutrits for physics at the LHC
This work develops a quantum machine learning model using qutrits (3-level quantum systems) for anomaly detection in high-energy physics data from the LHC, specifically CMS. By using Majorana encoding to represent qutrit states, the model shows a greater capacity to distinguish between different signal types compared to qubit models. The research validates this approach, identifying specific decay signals like t -> bqq bar as the most anomalous.