Quantum Wasserstein GANs for State Preparation at Unseen Points of a
  Phase Diagram
Wiktor Jurasz Christian B. Mendl
Generative models and in particular Generative Adversarial Networks (GANs)
have become very popular and powerful data generation tool. In recent years,
major progress has been made in extending this concept into the quantum realm.
However, most of the current methods focus on generating classes of states that
were supplied in the input set and seen at the training time. In this work, we
propose a new hybrid classical-quantum method based on quantum Wasserstein GANs
that overcomes this limitation. It allows to learn the function governing the
measurement expectations of the supplied states and generate new states, that
were not a part of the input set, but which expectations follow the same
underlying function.