Cargando…
Generalization properties of neural network approximations to frustrated magnet ground states
Neural quantum states (NQS) attract a lot of attention due to their potential to serve as a very expressive variational ansatz for quantum many-body systems. Here we study the main factors governing the applicability of NQS to frustrated magnets by training neural networks to approximate ground stat...
Autores principales: | Westerhout, Tom, Astrakhantsev, Nikita, Tikhonov, Konstantin S., Katsnelson, Mikhail I., Bagrov, Andrey A. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7101385/ https://www.ncbi.nlm.nih.gov/pubmed/32221284 http://dx.doi.org/10.1038/s41467-020-15402-w |
Ejemplares similares
-
Approximating Ground States by Neural Network Quantum States
por: Yang, Ying, et al.
Publicado: (2019) -
Simulations of frustrated Ising Hamiltonians using quantum approximate optimization
por: Lotshaw, Phillip C., et al.
Publicado: (2023) -
Towards photonic quantum simulation of ground states of frustrated Heisenberg spin systems
por: Ma, Xiao-song, et al.
Publicado: (2014) -
Diffusive excitonic bands from frustrated triangular sublattice in a singlet-ground-state system
por: Gao, Bin, et al.
Publicado: (2023) -
Magnetic frustration of graphite oxide
por: Lee, Dongwook, et al.
Publicado: (2017)