Hu Ling, Wu Shu-Hao, Cai Weizhou, Ma Yuwei, Mu Xianghao, Xu Yuan, Wang Haiyan, Song Yipu, Deng Dong-Ling, Zou Chang-Ling, Sun Luyan
Center for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China.
Key Laboratory of Quantum Information, CAS, University of Science and Technology of China, Hefei, Anhui 230026, China.
Sci Adv. 2019 Jan 25;5(1):eaav2761. doi: 10.1126/sciadv.aav2761. eCollection 2019 Jan.
Generative adversarial learning is one of the most exciting recent breakthroughs in machine learning. It has shown splendid performance in a variety of challenging tasks such as image and video generation. More recently, a quantum version of generative adversarial learning has been theoretically proposed and shown to have the potential of exhibiting an exponential advantage over its classical counterpart. Here, we report the first proof-of-principle experimental demonstration of quantum generative adversarial learning in a superconducting quantum circuit. We demonstrate that, after several rounds of adversarial learning, a quantum-state generator can be trained to replicate the statistics of the quantum data output from a quantum channel simulator, with a high fidelity (98.8% on average) so that the discriminator cannot distinguish between the true and the generated data. Our results pave the way for experimentally exploring the intriguing long-sought-after quantum advantages in machine learning tasks with noisy intermediate-scale quantum devices.
生成对抗学习是机器学习领域近年来最令人兴奋的突破之一。它在图像和视频生成等各种具有挑战性的任务中表现出色。最近，理论上提出了一种量子版本的生成对抗学习，并显示出相对于经典版本具有指数优势的潜力。在此，我们报告了在超导量子电路中量子生成对抗学习的首个原理验证实验演示。我们证明，经过几轮对抗学习后，可以训练一个量子态生成器来复制从量子信道模拟器输出的量子数据的统计信息，保真度很高（平均为98.8%），以至于鉴别器无法区分真实数据和生成的数据。我们的结果为利用有噪声的中等规模量子设备在机器学习任务中实验探索长期以来令人感兴趣的量子优势铺平了道路。
