  We introduce an architecture for neural quantum states for many-body
quantum-mechanical systems, based on normalizing flows. The use of normalizing
flows enables efficient uncorrelated sampling of configurations from the
probability distribution defined by the wavefunction, mitigating a major cost
of using neural states in simulation. We demonstrate the use of this
architecture for both ground-state preparation (for self-interacting particles
in a harmonic trap) and real-time evolution (for one-dimensional tunneling).
Finally, we detail a procedure for obtaining rigorous estimates of the
systematic error when using neural states to approximate quantum evolution.
