Machine learning quantum phases of matter beyond the fermion sign problem
Abstract State-of-the-art machine learning techniques promise to become a powerful tool in statistical mechanics via their capacity to distinguish different phases of matter in an automated way. Here we demonstrate that convolutional neural networks (CNN) can be optimized for quantum many-fermion sy...
Enregistré dans:
Auteurs principaux: | Peter Broecker, Juan Carrasquilla, Roger G. Melko, Simon Trebst |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2017
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/2436c67e977c43d480a0a608687695d2 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Symmetry Classes of Open Fermionic Quantum Matter
par: Alexander Altland, et autres
Publié: (2021) -
Fermion-induced quantum critical points
par: Zi-Xiang Li, et autres
Publié: (2017) -
Generalized lattice Wilson–Dirac fermions in (1 + 1) dimensions for atomic quantum simulation and topological phases
par: Yoshihito Kuno, et autres
Publié: (2018) -
Quantum transport evidence of Weyl fermions in an epitaxial ferromagnetic oxide
par: Kosuke Takiguchi, et autres
Publié: (2020) -
Quantum kinetic equation for fluids of spin-1/2 fermions
par: Ömer F. Dayi, et autres
Publié: (2021)