118 citations · 280 across the 17 of their papers we have counts for
4 papers · 1 filter
Computing quantum entanglement with machine learning
Andrea Bulgarelli, Elia Cellini, Karl Jansen +5
Entanglement calculations in quantum field theories are extremely challenging and typically rely on the replica trick, where the problem is rephrased in a study of defects. We demo…
Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories
Kim A. Nicoli, Christopher J. Anders, Tobias Hartung +3
We study the consequences of mode-collapse of normalizing flows in the context of lattice field theory. Normalizing flows allow for independent sampling. For this reason, it is hop…
Machine Learning of Thermodynamic Observables in the Presence of Mode Collapse
Kim A. Nicoli, Christopher Anders, Lena Funcke +5
Estimating the free energy, as well as other thermodynamic observables, is a key task in lattice field theories. Recently, it has been pointed out that deep generative models can b…
Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models
Kim A. Nicoli, Christopher J. Anders, Lena Funcke +5
In this work, we demonstrate that applying deep generative machine learning models for lattice field theory is a promising route for solving problems where Markov Chain Monte Carlo…