I present a heuristic to simulate quantum circuits based on a probabilistic representation of the quantum state as the outcome distribution of a positive operator valued measure. In this language, unitary evolution translates into evolution of probability distributions subject to somewhat stochastic matrices, which are a generalization of stochastic matrices. I approximate their evolution using recurrent neural networks and provide a proof-of-principle demonstration of the approach on simple quantum circuits.