A hybrid quantum-classical conditional generative adversarial network algorithm for human-centered paradigm in cloud
This is an incremental improvement for human-centered computing in cloud environments, addressing the randomness and lack of human interaction in existing QGANs.
The paper tackles the problem of making quantum generative adversarial networks (QGANs) more suitable for human-centered computing by proposing a hybrid quantum-classical conditional GAN (QCGAN) that stabilizes generation and enables human interaction through conditional inputs. The result shows that QCGAN effectively converges to Nash equilibrium and performs human-centered classification generation tasks on a quantum cloud platform.
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.