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Quantum Generative Adversarial Networks in a Continuous-Variable Architecture to Simulate High Energy Physics Detectors
Deep Neural Networks (DNNs) come into the limelight in High Energy Physics (HEP) in order to manipulate the increasing amount of data encountered in the next generation of accelerators. Recently, the HEP community has suggested Generative Adversarial Networks (GANs) to replace traditional time-consu...
| Autores principales: | , , , |
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| Lenguaje: | eng |
| Publicado: |
2021
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| Materias: | |
| Acceso en línea: | http://cds.cern.ch/record/2751529 |