  We propose a new variational scheme based on the neural-network quantum
states to simulate the stationary states of open quantum many-body systems.
Using the high expressive power of the variational ansatz described by the
restricted Boltzmann machines, which we dub as the neural stationary state
ansatz, we compute the stationary states of quantum dynamics obeying the
Lindblad master equations. The mapping of the stationary-state search problem
into finding a zero-energy ground state of an appropriate Hermitian operator
allows us to apply the conventional variational Monte Carlo method for the
optimization. Our method is shown to simulate various spin systems efficiently,
i.e., the transverse-field Ising models in both one and two dimensions and the
XYZ model in one dimension.
