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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2410.12650 |
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| _version_ | 1866913548937986048 |
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| author | Delgado, Andrea Venegas-Vargas, Diego Huynh, Adam Carroll, Kevon |
| author_facet | Delgado, Andrea Venegas-Vargas, Diego Huynh, Adam Carroll, Kevon |
| contents | Generative modeling for high-resolution images in Liquid Argon Time Projection Chambers (LArTPC), used in neutrino physics experiments, presents significant challenges due to the complexity and sparsity of the data. This work explores the application of quantum-enhanced generative networks to address these challenges, focusing on the scaling of models to handle larger image sizes and avoid the often encountered problem of mode collapse. To counteract mode collapse, regularization methods were introduced and proved to be successful on small-scale images, demonstrating improvements in stabilizing the training process. Although mode collapse persisted in higher-resolution settings, the introduction of these techniques significantly enhanced the model's performance in lower-dimensional cases, providing a strong foundation for further exploration. These findings highlight the potential for quantum-enhanced generative models in LArTPC data generation and offer valuable insights for the future development of scalable hybrid quantum-classical solutions in nuclear and high-energy physics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12650 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Designing Scalable Quantum-Enhanced Generative Networks for Neutrino Physics Experiments with Liquid Argon Time Projection Chambers Delgado, Andrea Venegas-Vargas, Diego Huynh, Adam Carroll, Kevon Quantum Physics High Energy Physics - Experiment Generative modeling for high-resolution images in Liquid Argon Time Projection Chambers (LArTPC), used in neutrino physics experiments, presents significant challenges due to the complexity and sparsity of the data. This work explores the application of quantum-enhanced generative networks to address these challenges, focusing on the scaling of models to handle larger image sizes and avoid the often encountered problem of mode collapse. To counteract mode collapse, regularization methods were introduced and proved to be successful on small-scale images, demonstrating improvements in stabilizing the training process. Although mode collapse persisted in higher-resolution settings, the introduction of these techniques significantly enhanced the model's performance in lower-dimensional cases, providing a strong foundation for further exploration. These findings highlight the potential for quantum-enhanced generative models in LArTPC data generation and offer valuable insights for the future development of scalable hybrid quantum-classical solutions in nuclear and high-energy physics. |
| title | Towards Designing Scalable Quantum-Enhanced Generative Networks for Neutrino Physics Experiments with Liquid Argon Time Projection Chambers |
| topic | Quantum Physics High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2410.12650 |