  Adversarial learning is one of the most successful approaches to modelling
high-dimensional probability distributions from data. The quantum computing
community has recently begun to generalize this idea and to look for potential
applications. In this work, we derive an adversarial algorithm for the problem
of approximating an unknown quantum pure state. Although this could be done on
universal quantum computers, the adversarial formulation enables us to execute
the algorithm on near-term quantum computers. Two parametrized circuits are
optimized in tandem: One tries to approximate the target state, the other tries
to distinguish between target and approximated state. Supported by numerical
simulations, we show that resilient backpropagation algorithms perform
remarkably well in optimizing the two circuits. We use the bipartite
entanglement entropy to design an efficient heuristic for the stopping
criterion. Our approach may find application in quantum state tomography.
