Efficient representation of quantum many-body states with deep neural networks
One of the challenges in studies of quantum many-body physics is finding an efficient way to record the large system wavefunctions. Here the authors present an analysis of the capabilities of recently-proposed neural network representations for storing physically accessible quantum states.
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Autores principales: | Xun Gao, Lu-Ming Duan |
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Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2017
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Materias: | |
Acceso en línea: | https://doaj.org/article/9f9c6842ec6b445d8d8f8a8a52fa8155 |
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