量子混合模型用更少参数生成真实海运航线图。
Investigating Parameter-Efficiency of Hybrid QuGANs Based on Geometric Properties of Generated Sea Route Graphs
- 用量子-经典混合生成对抗网络生成航线图。
- 小规模量子模型达到大模型经典GAN的生成质量。
- 适合关注量子计算效率与生成数据多样性的研究者。
为开发、训练和测试新算法，人工生成数据的需求广泛存在。量子计算（QC）因其固有的概率特性，有望在生成式人工智能领域发挥作用。本研究利用量子-经典混合生成对抗网络（QuGANs）生成航运路线图。基于真实航运数据构建训练集，探究QuGANs学习并再现数据内在分布与几何特征的能力。与经典生成对抗网络（GANs）对比，重点关注其参数效率。结果表明，尽管QuGANs在引入采样数据方差方面存在困难，但能快速学习并表征底层几何属性与分布。在参数数量上小于经典GAN的情况下，部分QuGANs仍能达到相近的生成质量。以航运数据生成为例，展示了量子计算在该领域的潜力与多样性。
原文摘要 · Abstract (English)
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.
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