235 citations · 316 across the 2 of their papers we have counts for
2 papers
cond-mat.quant-gas2021★ 81 cited
Unsupervised machine learning of topological phase transitions from experimental data
Niklas Käming, Anna Dawid, Korbinian Kottmann +4
Identifying phase transitions is one of the key challenges in quantum many-body physics. Recently, machine learning methods have been shown to be an alternative way of localising p…
cond-mat.quant-gas2018★ 235 cited
Identifying Quantum Phase Transitions using Artificial Neural Networks on Experimental Data
Benno S. Rem, Niklas Käming, Matthias Tarnowski +5
Machine learning techniques such as artificial neural networks are currently revolutionizing many technological areas and have also proven successful in quantum physics application…