Papers › Quantum Vision Transformers for Quark-Gluon Classification

Quantum Vision Transformers for Quark-Gluon Classification

16 May 2024arXiv:2405.10284archive 2025-07-28

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

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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datasets_to_dataloaders salcc/QuantumTransformers/quantum_transformers/qmlperfcomp/torch_backend/data.py official repository ran GPL-3.0 (copyleft) · pointer only · 43e9a05b4d5cb683 · report
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Tasks

ClassificationComputational Efficiency

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Methods

AttentionDense ConnectionsLayer NormalizationLinear LayerMulti-Head AttentionResidual ConnectionSoftmaxVision Transformer

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