Hybrid Quantum Vision Transformers for Event Classification in High Energy Physics

Abstract

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, 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 analogs with a similar number of parameters.

Quantum Vision Transformers for Quark–Gluon Classification

“…This approach is based on previous work [13], which proposed the same idea for the original transformer architecture for text [14]. Other works have explored other possible quantum adaptations of the original transformer [13,15], as well as adaptations of the vision transformer [16,17] and the graph transformer [18]. Our work differs from [16] in the architecture that we propose, which explores the use of other quantum ansatzes.…”

“…Our work differs from [16] in the architecture that we propose, which explores the use of other quantum ansatzes. The model in [17] was developed in parallel with this study and differs in several respects-the use of classical multi-layer perceptrons (MLPs) instead of quantum MLPs and the use of different ansatzes for the key, value, and query operations.…”