  While established neural network approaches based on restricted Boltzmann
machine architectures and Metropolis sampling methods are well suited for
symmetric open quantum systems, they result in poor scalability and systematic
errors for setups without symmetries of translational invariance, independent
of training parameters such as the sample size. To overcome this
representational limit, we present a hybrid sampling strategy which takes
asymmetric properties explicitly into account, achieving fast convergence times
and high scalability for asymmetric open systems, underlining the universal
applicability of artificial neural networks.
