  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.
