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Variational learning of quantum ground states on spiking neuromorphic hardware

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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: Klassert, Robert (VerfasserIn) , Baumbach, Andreas (VerfasserIn) , Petrovici, Mihai A. (VerfasserIn) , Gärttner, Martin (VerfasserIn)
Dokumenttyp: Article (Journal) Kapitel/Artikel
Sprache:Englisch
Veröffentlicht: November 29, 2021
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
Volltext
Verfasserangaben:Robert Klassert, Andreas Baumbach, Mihai A. Petrovici, and Martin Gärttner

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