19 citations · 34 across the 13 of their papers we have counts for
7 papers · 1 filter
Simulating Correlated Electrons with Symmetry-Enforced Normalizing Flows
Dominic Schuh, Janik Kreit, Evan Berkowitz +4
We present the first proof of principle that normalizing flows can accurately learn the Boltzmann distribution of the fermionic Hubbard model - a key framework for describing the e…
Search for Stable States in Two-Body Excitations of the Hubbard Model on the Honeycomb Lattice
Petar Sinilkov, Evan Berkowitz, Thomas Luu +1
We present one- and two-body measurements for the Hubbard model on the honeycomb (graphene) lattice from ab-initio quantum monte carlo simulations. Of particular interest is excito…
Simulating the Hubbard Model with Equivariant Normalizing Flows
Dominic Schuh, Janik Kreit, Evan Berkowitz +4
Generative models, particularly normalizing flows, have shown exceptional performance in learning probability distributions across various domains of physics, including statistical…
Reducing the Sign Problem with simple Contour Deformation
Christoph Gäntgen, Evan Berkowitz, Thomas Luu +2
We apply constant imaginary offsets to the path integral for a reduction of the sign problem in the Hubbard model. These simple transformations enhance the quality of results from…
Single Particle Spectrum of Doped -Perylene
Marcel Rodekamp, Evan Berkowitz, Christoph Gäntgen +4
We present a Hamiltonian Monte Carlo study of doped perylene described with the Hubbard model. Doped perylene can be used for organic light-emittin…
Fermionic Sign Problem Minimization by Constant Path Integral Contour Shifts
Christoph Gäntgen, Evan Berkowitz, Thomas Luu +2
The path integral formulation of quantum mechanical problems including fermions is often affected by a severe numerical sign problem. We show how such a sign problem can be allevia…
Mitigating the Hubbard Sign Problem. A Novel Application of Machine Learning
Marcel Rodekamp, Christoph Gäntgen
Many fascinating systems suffer from a severe (complex action) sign problem preventing us from calculating them with Markov Chain Monte Carlo simulations. One promising method to a…