  Noisy intermediate-scale quantum (NISQ) devices build the first generation of
quantum computers. Quantum neural networks (QNNs) gained high interest as one
of the few suitable quantum algorithms to run on these NISQ devices. Most of
the QNNs exploit supervised training algorithms with quantum states in form of
pairs to learn their underlying relation. However, only little attention has
been given to unsupervised training algorithms despite interesting applications
where the quantum data does not occur in pairs. Here we propose an approach to
unsupervised learning and reproducing characteristics of any given set of
quantum states. We build a generative adversarial model using two dissipative
quantum neural networks (DQNNs), leading to the dissipative quantum generative
adversarial network (DQGAN). The generator DQNN aims to produce quantum states
similar to the training data while the discriminator DQNN aims to distinguish
the generator's output from the training data. We find that training both parts
in a competitive manner results in a well trained generative DQNN. We see our
contribution as a proof of concept for using DQGANs to learn and extend
unlabeled training sets.
