Quantum Vision Transformers for Quark–Gluon Classification

Comajoan Cara, Marçal; Dahale, Gopal Ramesh; Dong, Zhongtian; Forestano, Roy T.; Gleyzer, Sergei; Justice, Daniel; Kong, Kyoungchul; Magorsch, Tom; Matchev, Konstantin T.; Matcheva, Katia; Unlu, Eyup B.

doi:10.3390/axioms13050323

Title: Quantum Vision Transformers for Quark–Gluon Classification

Journal Article · 13 May 2024 · Axioms

DOI: https://doi.org/10.3390/axioms13050323 · OSTI ID:2349540

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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.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: AC02-05CH11231; SC0012447; SC0022148; SC0024407

OSTI ID:: 2349540

Journal Information:: Axioms, Journal Name: Axioms Journal Issue: 5 Vol. 13; ISSN 2075-1680; ISSN AXIOB8

Publisher:: MDPI AGCopyright Statement

Country of Publication:: Country unknown/Code not available

Language:: English

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