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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Detalles Bibliográficos
Autores principales: Klassert, Robert, Baumbach, Andreas, Petrovici, Mihai A., Gärttner, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9386107/
https://www.ncbi.nlm.nih.gov/pubmed/35992070
http://dx.doi.org/10.1016/j.isci.2022.104707

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9386107/
https://www.ncbi.nlm.nih.gov/pubmed/35992070
http://dx.doi.org/10.1016/j.isci.2022.104707

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