一种学习多域数据联合分布的量子耦合生成对抗网络

doi:10.12305/j.issn.1001-506X.2026.03.05

Abstract

Abstract:

Ordinary quantum generative adversarial network （GAN） can only handle single domain data and are limited by the number of quantum bits, resulting in a significant decrease in generation quality. In regard to this, a quantum coupled GAN （QCoGAN） is proposed to learn the joint distribution of multi domain data. QCoGAN combines the parallel computing capability of quantum computing with the learning ability of classical GANs to couple and optimize the structure of quantum GANs. Compared with ordinary GANs, it can capture more image details. By applying weight sharing constraints between quantum parameter layers, it effectively controls the network capacity and improves training efficiency. Applying quantum GAN to domain adaptation problems and QCoGAN to joint distributed learning tasks on multiple handwritten datasets is demonstrated its superiority in handling multi domain data and has broad application prospects.

Key words: quantum computing, machine learning, generative adversarial network (GAN), quantum coupling, multi-domain data

CLC Number:

TN 929.5

Jiaxin JI, Ting LI, Fei LI. A quantum coupled generative adversarial network for learning the joint distribution of multi-domain data[J]. Systems Engineering and Electronics, 2026, 48(3): 779-786.

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Table 1

References 13

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数据库	[26]	[27]	[28]	[29]	QCoGAN
MNIST-USPS	0.408	0.467	0.478	0.607	0.756
USPS-MNIST	0.274	0.355	0.631	0.673	0.825
平均值	0.341	0.411	0.554	0.640	0.790

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A quantum coupled generative adversarial network for learning the joint distribution of multi-domain data

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