  The near-critical unitary dynamics of quantum Ising spin chains in
transversal and longitudinal magnetic fields is studied using an artificial
neural network representation of the wave function. A focus is set on strong
spatial correlations which build up in the system following a quench into the
vicinity of the quantum critical point. We compare correlations observed
following reinforcement learning of the network states with analytical
solutions in integrable cases and tDMRG simulations, as well as with
predictions from a semi-classical discrete Truncated Wigner analysis. While the
semi-classical approach excells mainly at short times and for small transverse
fields, the neural-network representation provides accurate results for a much
wider range of parameters. Where long-range spin-spin correlations build up in
the long-time dynamics we find qualitative agreement with exact results while
quantitative deviations are of similar size as for the semi-classically
predicted correlations, and slow convergence is observed when increasing the
number of hidden neurons.
