CaloQVAE : Simulating high-energy particle-calorimeter interactions using hybrid quantum-classical generative models
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916434193416192 |
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| author | Hoque, Sehmimul Jia, Hao Abhishek, Abhishek Fadaie, Mojde Toledo-Marín, J. Quetzalcoatl Vale, Tiago Melko, Roger G. Swiatlowski, Maximilian Fedorko, Wojciech T. |
| author_facet | Hoque, Sehmimul Jia, Hao Abhishek, Abhishek Fadaie, Mojde Toledo-Marín, J. Quetzalcoatl Vale, Tiago Melko, Roger G. Swiatlowski, Maximilian Fedorko, Wojciech T. |
| contents | The Large Hadron Collider's high luminosity era presents major computational challenges in the analysis of collision events. Large amounts of Monte Carlo (MC) simulation will be required to constrain the statistical uncertainties of the simulated datasets below these of the experimental data. Modelling of high-energy particles propagating through the calorimeter section of the detector is the most computationally intensive MC simulation task. We introduce a technique combining recent advancements in generative models and quantum annealing for fast and efficient simulation of high-energy particle-calorimeter interactions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_03179 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | CaloQVAE : Simulating high-energy particle-calorimeter interactions using hybrid quantum-classical generative models Hoque, Sehmimul Jia, Hao Abhishek, Abhishek Fadaie, Mojde Toledo-Marín, J. Quetzalcoatl Vale, Tiago Melko, Roger G. Swiatlowski, Maximilian Fedorko, Wojciech T. High Energy Physics - Experiment Machine Learning Quantum Physics 81P68, 68T07, 81V99 The Large Hadron Collider's high luminosity era presents major computational challenges in the analysis of collision events. Large amounts of Monte Carlo (MC) simulation will be required to constrain the statistical uncertainties of the simulated datasets below these of the experimental data. Modelling of high-energy particles propagating through the calorimeter section of the detector is the most computationally intensive MC simulation task. We introduce a technique combining recent advancements in generative models and quantum annealing for fast and efficient simulation of high-energy particle-calorimeter interactions. |
| title | CaloQVAE : Simulating high-energy particle-calorimeter interactions using hybrid quantum-classical generative models |
| topic | High Energy Physics - Experiment Machine Learning Quantum Physics 81P68, 68T07, 81V99 |
| url | https://arxiv.org/abs/2312.03179 |