Realizing a quantum generative adversarial network using a programmable superconducting processor

• URL: http://arxiv.org/abs/2009.12827v1
• Date: Sun, 27 Sep 2020 12:09:33 GMT
• Title: Realizing a quantum generative adversarial network using a programmable superconducting processor
• Authors: Kaixuan Huang, Zheng-An Wang, Chao Song, Kai Xu, Hekang Li, Zhen Wang, Qiujiang Guo, Zixuan Song, Zhi-Bo Liu, Dongning Zheng, Dong-Ling Deng, H. Wang, Jian-Guo Tian, and Heng Fan
• Abstract summary: We report an experimental implementation of a quantum generative adversarial network (QGAN) using a programmable superconducting processor. Our implementation is promising to scale up to noisy intermediate-scale quantum devices.
• Score: 17.3986929818418
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 multi-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.

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