11 citations · 14 across the 5 of their papers we have counts for
5 papers
Learning Minimal Representations of Fermionic Ground States
Felix Frohnert, Emiel Koridon, Stefano Polla
We introduce an unsupervised machine-learning framework that discovers optimally compressed representations of quantum many-body ground states. Using an autoencoder neural network…
Learning Pole Structures of Hadronic States using Predictive Uncertainty Estimation
Felix Frohnert, Denny Lane B. Sombillo, Evert van Nieuwenburg +1
Matching theoretical predictions to experimental data remains a central challenge in hadron spectroscopy. In particular, the identification of new hadronic states is difficult, as…
Discovering emergent connections in quantum physics research via dynamic word embeddings
Felix Frohnert, Xuemei Gu, Mario Krenn +1
As the field of quantum physics evolves, researchers naturally form subgroups focusing on specialized problems. While this encourages in-depth exploration, it can limit the exchang…
Learning Density Functionals from Noisy Quantum Data
Emiel Koridon, Felix Frohnert, Eric Prehn +3
The search for useful applications of noisy intermediate-scale quantum (NISQ) devices in quantum simulation has been hindered by their intrinsic noise and the high costs associated…
Explainable Representation Learning of Small Quantum States
Felix Frohnert, Evert van Nieuwenburg
Unsupervised machine learning models build an internal representation of their training data without the need for explicit human guidance or feature engineering. This learned repre…