Generative Neural Samplers for the Quantum Heisenberg Chain

Abstract

Generative neural samplers offer a complementary approach to Monte Carlo methods for problems in statistical physics and quantum field theory. This work 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 that are in good agreement with Monte Carlo results within the same approximation scheme.

arXiv comment10 figures

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Cited by in corpus (4)

• Flow-based sampling for fermionic lattice field theories
• Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning
• Sampling U(1) gauge theory using a re-trainable conditional flow-based model
• Application of the Variational Autoencoder to Detect the Critical Points of the Anisotropic Ising Model