  Models based on vision transformer architectures are considered
state-of-the-art when it comes to image classification tasks. However, they
require extensive computational resources both for training and deployment. The
problem is exacerbated as the amount and complexity of the data increases.
Quantum-based vision transformer models could potentially alleviate this issue
by reducing the training and operating time while maintaining the same
predictive power. Although current quantum computers are not yet able to
perform high-dimensional tasks yet, they do offer one of the most efficient
solutions for the future. In this work, we construct several variations of a
quantum hybrid vision transformer for a classification problem in high energy
physics (distinguishing photons and electrons in the electromagnetic
calorimeter). We test them against classical vision transformer architectures.
Our findings indicate that the hybrid models can achieve comparable performance
to their classical analogues with a similar number of parameters.
