  Large-scale quantum devices provide insights beyond the reach of classical
simulations. However, for a reliable and verifiable quantum simulation, the
building blocks of the quantum device require exquisite benchmarking. This
benchmarking of large scale dynamical quantum systems represents a major
challenge due to lack of efficient tools for their simulation. Here, we present
a scalable algorithm based on neural networks for Hamiltonian tomography in
out-of-equilibrium quantum systems. We illustrate our approach using a model
for a forefront quantum simulation platform: ultracold atoms in optical
lattices. Specifically, we show that our algorithm is able to reconstruct the
Hamiltonian of an arbitrary size quasi-1D bosonic system using an accessible
amount of experimental measurements. We are able to significantly increase the
previously known parameter precision.
