Variational learning of quantum ground states on spiking neuromorphic hardware
Recent research has demonstrated the usefulness of neural networks as variational ansatz functions for quantum many-body states. However, high-dimensional sampling spaces and transient autocorrelations confront these approaches with a challenging computational bottleneck. Compared to conventional ne...
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| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) Kapitel/Artikel |
| Sprache: | Englisch |
| Veröffentlicht: |
November 29, 2021
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| In: |
Arxiv
Year: 2021, Pages: 1-13 |
| DOI: | 10.48550/arXiv.2109.15169 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.48550/arXiv.2109.15169 Verlag, lizenzpflichtig, Volltext: http://arxiv.org/abs/2109.15169 
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| Verfasserangaben: | Robert Klassert, Andreas Baumbach, Mihai A. Petrovici, and Martin Gärttner |
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