Quantum Vision Transformers for Quark-Gluon Classification
This addresses resource constraints in particle physics data analysis, but is incremental as it matches rather than surpasses classical performance.
The paper tackles the challenge of computational efficiency for analyzing High Luminosity Large Hadron Collider data by introducing a hybrid quantum-classical vision transformer architecture, achieving classification performance for quark-gluon jets almost on par with classical models with similar parameters.
We introduce a hybrid quantum-classical vision transformer architecture, notable for its integration of variational quantum circuits within both the attention mechanism and the multi-layer perceptrons. The research addresses the critical challenge of computational efficiency and resource constraints in analyzing data from the upcoming High Luminosity Large Hadron Collider, presenting the architecture as a potential solution. In particular, we evaluate our method by applying the model to multi-detector jet images from CMS Open Data. The goal is to distinguish quark-initiated from gluon-initiated jets. We successfully train the quantum model and evaluate it via numerical simulations. Using this approach, we achieve classification performance almost on par with the one obtained with the completely classical architecture, considering a similar number of parameters.