activity
20222026
most citedMitigating the Hubbard Sign Problem with Complex-Valued Neural Networks

19 citations · 34 across the 13 of their papers we have counts for

collaborators

14 papers

hep-lat2026

Nucleon-sigma terms at with a variational analysis from lattice QCD

Lorenzo Barca, Gunnar Bali, Sara Collins +1

Nucleon sigma terms are important for the decomposition of the nucleon mass and for searches for new physics beyond the Standard Model involving scalar interactions. A persistent t…

hep-lat2026

Third moments of nucleon unpolarized, polarized, and transversity parton distribution functions from physical-point lattice QCD

Emilio Taggi, Michael Engelhardt, Jeremy R. Green +6

Using forward matrix elements of local leading-twist operators, we present a determination of the isovector third Mellin moments of nucleon unpolarized, polari…

cond-mat.str-el2025★ 1 cited

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…

cond-mat.dis-nn2025

Exploring Group Convolutional Networks for Sign Problem Mitigation via Contour Deformation

Christoph Gäntgen, Thomas Luu, Marcel Rodekamp

The sign problem that arises in Hybrid Monte Carlo calculations can be mitigated by deforming the integration manifold. While simple transformations are highly efficient for simula…

cond-mat.str-el2025

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…

cond-mat.str-el2025

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…

cond-mat.str-el2024★ 1 cited

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…

cond-mat.str-el2024★ 2 cited

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…

hep-lat2024

Quark orbital angular momentum in the proton from a twist-3 generalized parton distribution

M. Engelhardt, N. Hasan, S. Krieg +6

Quark orbital angular momentum in the proton is evaluated via a Lattice QCD calculation of the second Mellin moment of the twist-3 generalized parton distribution $\widetilde{E}_{2…

hep-lat2024★ 4 cited

Moments of Nucleon Unpolarized, Polarized, and Transversity Parton Distribution Functions from Lattice QCD at the Physical Point

Marcel Rodekamp, Michael Engelhardt, Jeremy R. Green +6

The second Mellin moments of the nucleon's unpolarized, polarized, and transversity parton distribution functions (PDFs) are computed. Two lattice QCD ensembles…

cond-mat.str-el2023★ 7 cited

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…

hep-lat2023

Moments of Parton Distributions Functions from Lattice QCD at the Physical Point

Marcel Rodekamp, Michael Engelhardt, Jeremy R. Green +5

We present a Lattice QCD calculation of the second Mellin moments of the nucleon axial, vector and tensor parton distribution functions (PDFs). The calculation is performed at the…

cond-mat.str-el2022

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…

physics.comp-ph2022★ 19 cited

Mitigating the Hubbard Sign Problem with Complex-Valued Neural Networks

Marcel Rodekamp, Evan Berkowitz, Christoph Gäntgen +3

Monte Carlo simulations away from half-filling suffer from a sign problem that can be reduced by deforming the contour of integration. Such a transformation, which induces a Jacobi…