Related references
Note: Only part of the references are listed.Stripes, Antiferromagnetism, and the Pseudogap in the Doped Hubbard Model at Finite Temperature
Alexander Wietek et al.
PHYSICAL REVIEW X (2021)
Optical lattice experiments at unobserved conditions with generative adversarial deep learning
Corneel Casert et al.
PHYSICAL REVIEW RESEARCH (2021)
Quantum many-body simulations of the two-dimensional Fermi-Hubbard model in ultracold optical lattices
Bin-Bin Chen et al.
PHYSICAL REVIEW B (2021)
One-component order parameter in URu2Si2 uncovered by resonant ultrasound spectroscopy and machine learning
Sayak Ghosh et al.
SCIENCE ADVANCES (2020)
Single-Exposure Absorption Imaging of Ultracold Atoms Using Deep Learning
Gal Ness et al.
PHYSICAL REVIEW APPLIED (2020)
Robust Bilayer Charge Pumping for Spin- and Density-Resolved Quantum Gas Microscopy
Joannis Koepsell et al.
PHYSICAL REVIEW LETTERS (2020)
Doublon-Hole Correlations and Fluctuation Thermometry in a Fermi-Hubbard Gas
Thomas Hartke et al.
PHYSICAL REVIEW LETTERS (2020)
Visualizing strange metallic correlations in the two-dimensional Fermi-Hubbard model with artificial intelligence
Ehsan Khatami et al.
PHYSICAL REVIEW A (2020)
Towards novel insights in lattice field theory with explainable machine learning
Stefan Bluecher et al.
PHYSICAL REVIEW D (2020)
Machine learning in electronic-quantum-matter imaging experiments
Yi Zhang et al.
NATURE (2019)
Classifying snapshots of the doped Hubbard model with machine learning
Annabelle Bohrdt et al.
NATURE PHYSICS (2019)
Identifying quantum phase transitions using artificial neural networks on experimental data
Benno S. Rem et al.
NATURE PHYSICS (2019)
String patterns in the doped Hubbard model
Christie S. Chiu et al.
SCIENCE (2019)
Supervised machine learning of ultracold atoms with speckle disorder
S. Pilati et al.
SCIENTIFIC REPORTS (2019)
Imaging magnetic polarons in the doped Fermi-Hubbard model
Joannis Koepsell et al.
NATURE (2019)
Direct observation of incommensurate magnetism in Hubbard chains
Guillaume Salomon et al.
NATURE (2019)
Bad metallic transport in a cold atom Fermi-Hubbard system
Peter T. Brown et al.
SCIENCE (2019)
Probing hidden spin order with interpretable machine learning
Jonas Greitemann et al.
PHYSICAL REVIEW B (2019)
Interpretable machine learning for inferring the phase boundaries in a nonequilibrium system
C. Casert et al.
PHYSICAL REVIEW E (2019)
Microscopic spinon-chargon theory of magnetic polarons in the t-J model
Fabian Grusdt et al.
PHYSICAL REVIEW B (2019)
Learning multiple order parameters with interpretable machines
Ke Liu et al.
PHYSICAL REVIEW B (2019)
Machine Learning Topological Invariants with Neural Networks
Pengfei Zhang et al.
PHYSICAL REVIEW LETTERS (2018)
A cold-atom Fermi-Hubbard antiferromagnet
Anton M. Azurenko et al.
NATURE (2017)
Probing many-body dynamics on a 51-atom quantum simulator
Hannes Bernien et al.
NATURE (2017)
Direct Observation of Dynamical Quantum Phase Transitions in an Interacting Many-Body System
P. Jurcevic et al.
PHYSICAL REVIEW LETTERS (2017)
Machine learning of explicit order parameters: From the Ising model to SU(2) lattice gauge theory
Sebastian J. Wetzel et al.
PHYSICAL REVIEW B (2017)
Tunable two-dimensional arrays of single Rydberg atoms for realizing quantum Ising models
Henning Labuhn et al.
NATURE (2016)
Regression shrinkage and selection via the lasso: a retrospective
Robert Tibshirani
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY (2011)
Single-atom-resolved fluorescence imaging of an atomic Mott insulator
Jacob F. Sherson et al.
NATURE (2010)
A quantum gas microscope for detecting single atoms in a Hubbard-regime optical lattice
Waseem S. Bakr et al.
NATURE (2009)