Quantum Vision Transformers for Quark-Gluon Classification
<|reference_start|>Quantum Vision Transformers for Quark-Gluon Classification: 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.<|reference_end|>
@article{cara2024quantum,
    title={Quantum Vision Transformers for Quark-Gluon Classification},
    author={Marc{c}al Comajoan Cara, Gopal Ramesh Dahale, Zhongtian Dong, Roy T.
  Forestano, Sergei Gleyzer, Daniel Justice, Kyoungchul Kong, Tom Magorsch,
  Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu},
    journal={Axioms 2024, 13(5), 323},
    year={2024},
    doi={10.3390/axioms13050323},
    archivePrefix={arXiv},
    eprint={2405.10284},
    primaryClass={quant-ph cs.LG hep-ph}
}