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.

提出了一种混合量子 - 经典视觉转换器架构，其特点是在注意机制和多层感知机中集成了变分量子电路。该研究解决了计算效率和资源限制在分析未来的高亮度大型强子对撞机数据方面的重要挑战，提出了该架构作为潜在解决方案。通过将该模型应用于 CMS 开放数据的多探测器喷注图像，我们评估了该方法的性能，旨在区分强子引发的喷注和胶子引发的喷注。我们成功训练了量子模型，并通过数值模拟对其进行了评估。使用这种方法，在考虑类似数量的参数的情况下，我们实现了与完全经典架构几乎相媲美的分类性能。