Normalizing flows provide a general approach to construct flexible variational posteriors. The parameters are learned by stochastic optimization of the variational bound, but inference can be slow due to high variance of the gradient estimator. We propose Quasi-Monte Carlo (QMC) flows which reduce the variance of the gradient estimator by one order of magnitude. First results show that QMC flows lead to faster inference and samples from the variational posterior cover the target space more evenly.