Convolutional transformer wave functions
Fuente:
arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866912273778343936 |
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| author | Chen, Ao Naik, Vighnesh Dattatraya Heyl, Markus |
| author_facet | Chen, Ao Naik, Vighnesh Dattatraya Heyl, Markus |
| contents | Deep neural quantum states have recently achieved remarkable performance in solving challenging quantum many-body problems. While transformer networks appear particularly promising due to their success in computer science, we show that previously reported transformer wave functions haven't so far been capable to utilize their full power. Here, we introduce the convolutional transformer wave function (CTWF). We show that our CTWFs exhibit superior performance in ground-state search and non-equilibrium dynamics compared to previous results, demonstrating promising capacity in complex quantum problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_10462 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Convolutional transformer wave functions Chen, Ao Naik, Vighnesh Dattatraya Heyl, Markus Disordered Systems and Neural Networks Quantum Physics Deep neural quantum states have recently achieved remarkable performance in solving challenging quantum many-body problems. While transformer networks appear particularly promising due to their success in computer science, we show that previously reported transformer wave functions haven't so far been capable to utilize their full power. Here, we introduce the convolutional transformer wave function (CTWF). We show that our CTWFs exhibit superior performance in ground-state search and non-equilibrium dynamics compared to previous results, demonstrating promising capacity in complex quantum problems. |
| title | Convolutional transformer wave functions |
| topic | Disordered Systems and Neural Networks Quantum Physics |
| url | https://arxiv.org/abs/2503.10462 |