Hybrid Quantum Vision Transformers for Event Classification in High Energy Physics
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909142903422976 |
|---|---|
| author | Unlu, Eyup B. Cara, Marçal Comajoan Dahale, Gopal Ramesh Dong, Zhongtian Forestano, Roy T. Gleyzer, Sergei Justice, Daniel Kong, Kyoungchul Magorsch, Tom Matchev, Konstantin T. Matcheva, Katia |
| author_facet | Unlu, Eyup B. Cara, Marçal Comajoan Dahale, Gopal Ramesh Dong, Zhongtian Forestano, Roy T. Gleyzer, Sergei Justice, Daniel Kong, Kyoungchul Magorsch, Tom Matchev, Konstantin T. Matcheva, Katia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00776 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hybrid Quantum Vision Transformers for Event Classification in High Energy Physics Unlu, Eyup B. Cara, Marçal Comajoan Dahale, Gopal Ramesh Dong, Zhongtian Forestano, Roy T. Gleyzer, Sergei Justice, Daniel Kong, Kyoungchul Magorsch, Tom Matchev, Konstantin T. Matcheva, Katia Quantum Physics Machine Learning High Energy Physics - Phenomenology 68Q12, 81P68 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. |
| title | Hybrid Quantum Vision Transformers for Event Classification in High Energy Physics |
| topic | Quantum Physics Machine Learning High Energy Physics - Phenomenology 68Q12, 81P68 |
| url | https://arxiv.org/abs/2402.00776 |