Quantum Vision Transformers for Quark-Gluon Classification

arXiv:2405.10284v1 Announce Type: cross
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 …

abstract architecture arxiv attention challenge circuits classification computational constraints cs.lg data efficiency hep-ph hybrid integration layer presenting quant-ph quantum research transformer transformer architecture transformers type vision vision transformers