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.

翻译：生成模型，特别是生成对抗网络（GANs），已成为非常流行且强大的数据生成工具。近年来，将该概念扩展到量子领域取得了重大进展。然而，当前大多数方法仅能生成输入集中训练阶段见过的量子态类别。在本工作中，我们提出一种基于量子Wasserstein生成对抗网络的新型混合经典-量子方法，克服了这一局限。该方法能够学习控制输入态测量期望值的函数，进而生成不属于输入集的新量子态，且其期望值遵循相同的底层函数。