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.

翻译：我们提出了一种混合量子-经典视觉transformer架构，其显著特点在于将变分量子电路同时集成至注意力机制与多层感知机中。该研究针对即将运行的高亮度大型强子对撞机数据分析中计算效率与资源约束的关键挑战，将该架构作为潜在解决方案进行评估。具体而言，我们通过将模型应用于CMS开放数据中的多探测器喷注图像进行方法验证，旨在区分夸克引发喷注与胶子引发喷注。我们成功训练了该量子模型并通过数值模拟对其进行评估。采用该方法，在参数规模相近的条件下，我们获得了与完全经典架构几乎相当的分类性能。