Generative adversarial networks are an emerging technique with wide applications in machine learning, which have achieved dramatic success in a number of challenging tasks including image and video generation. When equipped with quantum processors, their quantum counterparts—called quantum generative adversarial networks (QGANs)—may even exhibit exponential advantages in certain machine learning applications. Here, we report an experimental implementation of a QGAN using a programmable superconducting processor, in which both the generator and the discriminator are parameterized via layers of single- and two-qubit quantum gates. The programmed QGAN runs automatically several rounds of adversarial learning with quantum gradients to achieve a Nash equilibrium point, where the generator can replicate data samples that mimic the ones from the training set. Our implementation is promising to scale up to noisy intermediate-scale quantum devices, thus paving the way for experimental explorations of quantum advantages in practical applications with near-term quantum technologies.

生成对抗网络是一种在机器学习中具有广泛应用的新兴技术，在包括图像和视频生成在内的许多具有挑战性的任务中取得了巨大的成功。当配备量子处理器时，它们的量子对应物——称为量子生成对抗网络 (QGAN)——甚至可能在某些机器学习应用中表现出指数级的优势。在这里，我们报告了使用可编程超导处理器的 QGAN 的实验实现，其中生成器和鉴别器都通过单量子位和双量子位量子门层进行参数化。编程的 QGAN 自动运行多轮具有量子梯度的对抗性学习，以达到纳什平衡点，生成器可以复制模拟训练集中样本的数据样本。我们的实现有望扩大到嘈杂的中等规模量子设备，从而为使用近期量子技术在实际应用中实验探索量子优势铺平道路。