标题： 基于生成海路图几何特性的混合QuGAN参数效率研究 显示英文标题

标题： Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs

摘要： 人工生成数据在新算法的开发、训练和测试中的需求无处不在。 量子计算（QC）提供了希望，其固有的概率功能可以在这个生成式人工智能领域得到利用。 在本研究中，我们使用量子-经典混合生成对抗网络（QuGANs）来人工生成航运路线图。 我们基于真实航运数据创建了一个训练数据集，并研究QuGANs在多大程度上能够学习并再现该数据的内在分布和几何特征。 我们将混合QuGANs与经典生成对抗网络（GANs）进行比较，特别关注它们的参数效率。 我们的结果表明， QuGAN确实能够快速学习并表示底层的几何属性和分布，尽管它们似乎在向采样数据中引入方差方面存在困难。 与参数数量较多的经典GANs相比，一些QuGANs表现出相似的结果质量。 我们对具体用例的参考，例如航运数据的生成，提供了一个生动的例子，并展示了QC可以使用的潜力和多样性。 显示英文摘要

摘要： The demand for artificially generated data for the development, training and testing of new algorithms is omnipresent. Quantum computing (QC), does offer the hope that its inherent probabilistic functionality can be utilised in this field of generative artificial intelligence. In this study, we use quantum-classical hybrid generative adversarial networks (QuGANs) to artificially generate graphs of shipping routes. We create a training dataset based on real shipping data and investigate to what extent QuGANs are able to learn and reproduce inherent distributions and geometric features of this data. We compare hybrid QuGANs with classical Generative Adversarial Networks (GANs), with a special focus on their parameter efficiency. Our results indicate that QuGANs are indeed able to quickly learn and represent underlying geometric properties and distributions, although they seem to have difficulties in introducing variance into the sampled data. Compared to classical GANs of greater size, measured in the number of parameters used, some QuGANs show similar result quality. Our reference to concrete use cases, such as the generation of shipping data, provides an illustrative example and demonstrate the potential and diversity in which QC can be used.