2020/12/18 by Johanna Vielhaben, Nils Strodthoff · 7 citations
Computer Science · Mathematics · Physics and Astronomy · #Anisotropy #Heisenberg model #Isotropy #Markov chain Monte Carlo #Mathematics #Monte Carlo method #Physics #Quantum #Quantum Monte Carlo #Quantum many-body systems #Quantum mechanics #Statistical Mechanics and Entropy #Statistical physics #Statistics #Theoretical and Computational Physics #cond-mat.stat-mech #cs.LG #stat.ML
paper · pdf · doi:10.1103/physreve.103.063304
published in Physical review. E 103(6), 063304 (American Physical Society) · 10 figures
arxiv created 2020/12/18 · openalex publication_date 2021/06/07 · arxiv updated 2021/06/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Generative neural samplers offer a complementary approach to Monte Carlo methods for problems in statistical physics and quantum field theory. This paper tests the ability of generative neural samplers to estimate observables for real-world low-dimensional spin systems. It maps out how autoregressive models can sample configurations of a quantum Heisenberg chain via a classical approximation based on the Suzuki-Trotter transformation. We present results for energy, specific heat, and susceptibility for the isotropic XXX and the anisotropic XY chain are in good agreement with Monte Carlo results within the same approximation scheme.