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

TL;DR

This paper explores the use of hybrid quantum-classical GANs to generate shipping route graphs, demonstrating their ability to learn geometric features efficiently and comparing their performance to classical GANs.

Contribution

It introduces a novel application of QuGANs for graph data generation and evaluates their parameter efficiency and ability to learn geometric properties.

Findings

QuGANs can learn and reproduce geometric features of shipping data

Some QuGANs match classical GANs in result quality with fewer parameters

QuGANs have difficulty introducing variance into generated data

Abstract

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.