期刊
NPJ QUANTUM INFORMATION
卷 7, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41534-021-00503-1
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资金
- National Basic Research Program of China [2017YFA0304300, 2016YFA0302104, 2016YFA0300600]
- National Natural Science Foundation of China [11934018, 11725419]
- Zhejiang Province Key Research and Development Program [2020C01019]
- Tsinghua University [53330300320]
- Strategic Priority Research Program of Chinese Academy of Sciences [XDB28000000]
- Shanghai Qi Zhi Institute
Generative adversarial networks have been successful in machine learning, and their quantum counterparts, known as quantum generative adversarial networks (QGANs), may have exponential advantages. Researchers have implemented a QGAN using a programmable superconducting processor, paving the way for experimental explorations of quantum advantages in practical applications with near-term quantum technologies.
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.
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