Generative adversarial networks (GANs) represent a powerful tool for
classical machine learning: a generator tries to create statistics for data
that mimics those of a true data set, while a discriminator tries to
discriminate between the true and fake data. The learning process for generator
and discriminator can be thought of as an adversarial game, and under
reasonable assumptions, the game converges to the point where the generator
generates the same statistics as the true data and the discriminator is unable
to discriminate between the true and the generated data. This paper introduces
the notion of quantum generative adversarial networks (QuGANs), where the data
consists either of quantum states, or of classical data, and the generator and
discriminator are equipped with quantum information processors. We show that
the unique fixed point of the quantum adversarial game also occurs when the
generator produces the same statistics as the data. Since quantum systems are
intrinsically probabilistic the proof of the quantum case is different from -
and simpler than - the classical case. We show that when the data consists of
samples of measurements made on high-dimensional spaces, quantum adversarial
networks may exhibit an exponential advantage over classical adversarial
networks.

本研究提出了一种新的量子生成对抗网络（QuGANs），其中生成器和鉴别器配备了量子信息处理器，当数据由量子状态或经典数据组成时，该方法达到唯一固定点使生成器产生与数据相同的统计数据。