Quantum machine learning is expected to be one of the first practical applications of near-term quantum devices. Pioneer theoretical works suggest that quantum generative adversarial networks (GANs) may exhibit a potential exponential advantage over classical GANs, thus attracting widespread attention. However, it remains elusive whether quantum GANs implemented on near-term quantum devices can actually solve real-world learning tasks. Here, we devise a flexible quantum GAN scheme to narrow this knowledge gap, which could accomplish image generation with arbitrarily high-dimensional features, and could also take advantage of quantum superposition to train multiple examples in parallel. For the first time, we experimentally achieve the learning and generation of real-world hand-written digit images on a superconducting quantum processor. Moreover, we utilize a gray-scale bar dataset to exhibit the competitive performance between quantum GANs and the classical GANs based on multilayer perceptron and convolutional neural network architectures, respectively, benchmarked by the Fr\'echet Distance score. Our work provides guidance for developing advanced quantum generative models on near-term quantum devices and opens up an avenue for exploring quantum advantages in various GAN-related learning tasks.
翻译:量子机器学习预计将成为短期量子装置的第一批实际应用之一。先锋理论研究表明,量子基因对抗网络(GANs)可能比古典GANs具有潜在的指数优势,从而引起广泛的关注。然而,在短期量子装置上实施的量子GANs能否真正解决现实世界的学习任务仍然难以确定。在这里,我们设计了一个灵活的量子GAN计划,以缩小这种知识差距,这种差距可以以任意的高维特征完成图像生成,也可以利用量子超定位来同时培训多个实例。我们第一次在超导量子处理器上实验了真实世界手写数字图像的学习和生成。此外,我们利用一个灰度条数据集展示量子GANs和古典GANs之间的竞争性表现,它们分别以Fr\'echet距离分为基准,我们的工作为开发关于近期量子装置的先进量子基因模型提供了指导,并为探索各种GAN相关学习任务中的量子优势开辟了一条途径。