As an emerging field that aims to bridge the gap between human activities and computing systems, human-centered computing (HCC) in cloud, edge, fog has had a huge impact on the artificial intelligence algorithms. The quantum generative adversarial network (QGAN) is considered to be one of the quantum machine learning algorithms with great application prospects, which also should be improved to conform to the human-centered paradigm. The generation process of QGAN is relatively random and the generated model does not conform to the human-centered concept, so it is not quite suitable for real scenarios. In order to solve these problems, a hybrid quantum-classical conditional generative adversarial network (QCGAN) algorithm is proposed, which is a knowledge-driven human–computer interaction computing mode that can be implemented in cloud. The purposes of stabilizing the generation process and realizing the interaction between human and computing process are achieved by inputting artificial conditional information in the generator and discriminator. The generator uses the parameterized quantum circuit with an all-to-all connected topology, which facilitates the tuning of network parameters during the training process. The discriminator uses the classical neural network, which effectively avoids the “input bottleneck” of quantum machine learning. Finally, the BAS training set is selected to conduct experiment on the quantum cloud computing platform. The result shows that the QCGAN algorithm can effectively converge to the Nash equilibrium point after training and perform human-centered classification generation tasks.

作为旨在弥合人类活动与计算系统之间的鸿沟的新兴领域，云，边缘，雾中的以人为中心的计算对人工智能算法产生了巨大影响。量子生成对抗网络（QGAN）被认为是具有广阔应用前景的量子机器学习算法之一，还应进行改进以符合以人为本的范式。QGAN的生成过程是相对随机的，并且生成的模型不符合以人为中心的概念，因此它不太适合真实场景。为了解决这些问题，提出了一种混合的量子经典条件生成对抗网络（QCGAN）算法，该算法是一种知识驱动的人机交互计算模式，可以在云中实现。通过在生成器和鉴别器中输入人工条件信息，可以达到稳定生成过程以及实现人与计算过程之间交互的目的。生成器使用具有全连接拓扑的参数化量子电路，这有助于在训练过程中调整网络参数。鉴别器使用经典的神经网络，可有效避免量子机器学习的“输入瓶颈”。最后，选择BAS训练集在量子云计算平台上进行实验。结果表明，训练后的QCGAN算法可以有效收敛到纳什均衡点，并以人为中心进行分类生成任务。