5 citations · 7 across the 12 of their papers we have counts for
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Data Free Training of Diffusion Models for Lattice Field Theories
Dennis Grinwald, Ankur Singha, Khaled Kahouli +2
Sampling equilibrium field configurations remains a major computational bottleneck in lattice field theory, motivating the development of generative samplers that can produce indep…
Multilevel Plaquette-Space Sampling for Lattice Gauge Theories with Local Constraint Solves
Ankur Singha, Jacob Finkenrath, Karl Jansen +2
Lattice gauge theories are an important class of physical models with high-dimensional structured distributions that underpin first-principles calculations in particle, nuclear, an…
Sector-Resolved Flow Sampling for Topologically Frozen Lattice Gauge Theories
Ankur Singha, Jacob Kauffmann, Karl Jansen +3
Topological fluctuations are essential to nonperturbative gauge theories but become increasingly difficult to sample toward the continuum limit, where Markov chains can freeze in s…
Scalable Generative Sampling and Multilevel Estimation for Lattice Field Theories Near Criticality
A. Singha, J. Kauffmann, E. Cellini +2
Sampling lattice field theories near criticality is severely hindered by critical slowing down, which makes standard Markov chain methods increasingly inefficient at large lattice…
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…
Small-scale Hamiltonian optimization of interpolating operators for Lagrangian lattice quantum field theory
Artur Avkhadiev, Lena Funcke, Karl Jansen +2
Lattice quantum field theory calculations may potentially combine the advantages of Hamiltonian formulations with the scalability and control of conventional Lagrangian frameworks.…