  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.
