{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/quantum-vision-transformers-for-quark-gluon","title":"Quantum Vision Transformers for Quark-Gluon Classification","arxiv_id":"2405.10284","date":"2024-05-16","proceeding":null,"authors":["Marçal 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"],"abstract":"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. 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