  Inspired by the universal approximation theorem and widespread adoption of
artificial neural network techniques in a diversity of fields, we propose
feed-forward neural networks as a general purpose trial wave function for
quantum Monte Carlo simulations of continous many-body systems. Whereas for
simple model systems the whole many-body wave function can be represented by a
neural network, the antisymmetry condition of non-trivial fermionic systems is
incorporated by means of a Slater determinant. To demonstrate the accuracy of
our trial wave functions, we have studied an exactly solvable model system of
two trapped interacting particles, as well as the hydrogen dimer.
