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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2023
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2302.12906 |
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| _version_ | 1866909216159039488 |
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| author | Rousselot, Armand Spannowsky, Michael |
| author_facet | Rousselot, Armand Spannowsky, Michael |
| contents | Invertible Neural Networks (INN) have become established tools for the simulation and generation of highly complex data. We propose a quantum-gate algorithm for a Quantum Invertible Neural Network (QINN) and apply it to the LHC data of jet-associated production of a Z-boson that decays into leptons, a standard candle process for particle collider precision measurements. We compare the QINN's performance for different loss functions and training scenarios. For this task, we find that a hybrid QINN matches the performance of a significantly larger purely classical INN in learning and generating complex data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_12906 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Generative Invertible Quantum Neural Networks Rousselot, Armand Spannowsky, Michael High Energy Physics - Phenomenology Artificial Intelligence Machine Learning Quantum Physics Invertible Neural Networks (INN) have become established tools for the simulation and generation of highly complex data. We propose a quantum-gate algorithm for a Quantum Invertible Neural Network (QINN) and apply it to the LHC data of jet-associated production of a Z-boson that decays into leptons, a standard candle process for particle collider precision measurements. We compare the QINN's performance for different loss functions and training scenarios. For this task, we find that a hybrid QINN matches the performance of a significantly larger purely classical INN in learning and generating complex data. |
| title | Generative Invertible Quantum Neural Networks |
| topic | High Energy Physics - Phenomenology Artificial Intelligence Machine Learning Quantum Physics |
| url | https://arxiv.org/abs/2302.12906 |