  Modeling quantum many-body systems is enormously challenging due to the
exponential scaling of Hilbert dimension with system size. Finding efficient
compressions of the wavefunction is key to building scalable models. Here, we
introduce iterative retraining, an approach for simulating bulk quantum systems
that uses recurrent neural networks (RNNs). By mapping translations in the
lattice vector to the time index of an RNN, we are able to efficiently capture
the near translational invariance of large lattices. We show that we can use
this symmetry mapping to simulate very large systems in one and two dimensions.
We do so by 'growing' our model, iteratively retraining the same model on
progressively larger lattices until edge effects become negligible. We argue
that this scheme generalizes more naturally to higher dimensions than Density
Matrix Renormalization Group.
