13 papers
Tackling the Sign Problem in the Doped Hubbard Model with Normalizing Flows
Dominic Schuh, Lena Funcke, Janik Kreit +2
The Hubbard model at finite chemical potential is a cornerstone for understanding doped correlated systems, but simulations are severely limited by the sign problem. In the auxilia…
Hamiltonian Monte Carlo enhanced by Exact Diagonalization
Finn L. Temmen, Martina Gisti, David J. Luitz +2
Strongly correlated fermionic systems are of great interest in condensed matter physics and numerical methods are indispensable tools for their study. However, existing approaches…
Defect engineering spin centers in interacting many-body Su-Schrieffer-Heeger chains
Lin Wang, Thomas Luu, Ulf-G. MeiÃner
The ability to engineer topologically distinct materials opens the possibility of enabling novel phenomena in low-dimensional nano-systems, as well as manufacturing novel quantum d…
Toward Scalable Normalizing Flows for the Hubbard Model
Janik Kreit, Andrea Bulgarelli, Lena Funcke +4
Normalizing flows have recently demonstrated the ability to learn the Boltzmann distribution of the Hubbard model, opening new avenues for generative modeling in condensed matter p…
Fully ergodic simulations using radial updates
Finn L. Temmen, Evan Berkowitz, Anthony Kennedy +3
A sensible application of the Hybrid Monte Carlo (HMC) method is often hindered by the presence of large - or even infinite - potential barriers. These potential barriers separate…
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…