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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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Main Authors: Klassert, Robert (Author) , Baumbach, Andreas (Author) , Petrovici, Mihai A. (Author) , Gärttner, Martin (Author)
Format: Article (Journal)
Language:English
Published: [August 19, 2022]
In: iScience
Year: 2022, Volume: 25, Issue: 8, Pages: 1-22
ISSN:2589-0042
DOI:10.1016/j.isci.2022.104707
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.isci.2022.104707
Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S2589004222009798
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Author Notes:Robert Klassert, Andreas Baumbach, Mihai A. Petrovici, and Martin Gärttner
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Variational learning of quantum ground states on spiking neuromorphic hardware by Klassert, Robert (Author) , Baumbach, Andreas (Author) , Petrovici, Mihai A. (Author) , Gärttner, Martin (Author) ,


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