  Variational Monte Carlo with neural network quantum states has proven to be a
promising avenue for evaluating the ground state energy of spin Hamiltonians.
However, despite continuous efforts the performance of the method on frustrated
Hamiltonians remains significantly worse than those on stoquastic Hamiltonians
that are sign-free. We present a detailed and systematic study of restricted
Boltzmann machine (RBM) based variational Monte Carlo for quantum spin chains,
resolving how relevant stoquasticity is in this setting. We show that in most
cases, when the Hamiltonian is phase connected with a stoquastic point, the
complex RBM state can faithfully represent the ground state, and local
quantities can be evaluated efficiently by sampling. On the other hand, we
identify several new phases that are challenging for the RBM Ansatz, including
non-topological robust non-stoquastic phases as well as stoquastic phases where
sampling is nevertheless inefficient. Furthermore, we find that an accurate
neural network representation of ground states in non-stoquastic phases is
hindered not only by the sign structure but also by their amplitudes.
