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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.21082 |
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| _version_ | 1866915085370261504 |
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| author | Baidachna, Mariia Guadarrama, Rey Dahale, Gopal Ramesh Magorsch, Tom Pedraza, Isabel Matchev, Konstantin T. Matcheva, Katia Kong, Kyoungchul Gleyzer, Sergei |
| author_facet | Baidachna, Mariia Guadarrama, Rey Dahale, Gopal Ramesh Magorsch, Tom Pedraza, Isabel Matchev, Konstantin T. Matcheva, Katia Kong, Kyoungchul Gleyzer, Sergei |
| contents | Diffusion models have demonstrated remarkable success in image generation, but they are computationally intensive and time-consuming to train. In this paper, we introduce a novel diffusion model that benefits from quantum computing techniques in order to mitigate computational challenges and enhance generative performance within high energy physics data. The fully quantum diffusion model replaces Gaussian noise with random unitary matrices in the forward process and incorporates a variational quantum circuit within the U-Net in the denoising architecture. We run evaluations on the structurally complex quark and gluon jets dataset from the Large Hadron Collider. The results demonstrate that the fully quantum and hybrid models are competitive with a similar classical model for jet generation, highlighting the potential of using quantum techniques for machine learning problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_21082 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum Diffusion Model for Quark and Gluon Jet Generation Baidachna, Mariia Guadarrama, Rey Dahale, Gopal Ramesh Magorsch, Tom Pedraza, Isabel Matchev, Konstantin T. Matcheva, Katia Kong, Kyoungchul Gleyzer, Sergei Quantum Physics Machine Learning High Energy Physics - Phenomenology Diffusion models have demonstrated remarkable success in image generation, but they are computationally intensive and time-consuming to train. In this paper, we introduce a novel diffusion model that benefits from quantum computing techniques in order to mitigate computational challenges and enhance generative performance within high energy physics data. The fully quantum diffusion model replaces Gaussian noise with random unitary matrices in the forward process and incorporates a variational quantum circuit within the U-Net in the denoising architecture. We run evaluations on the structurally complex quark and gluon jets dataset from the Large Hadron Collider. The results demonstrate that the fully quantum and hybrid models are competitive with a similar classical model for jet generation, highlighting the potential of using quantum techniques for machine learning problems. |
| title | Quantum Diffusion Model for Quark and Gluon Jet Generation |
| topic | Quantum Physics Machine Learning High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2412.21082 |