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

2025-01-15

AI Summary

This study addresses the trade-off between parameter efficiency and geometric fidelity in nautical chart data generation, proposing QuGANs—the first quantum-classical hybrid generative adversarial network tailored for maritime trajectory graph modeling. Methodologically, real vessel trajectories are encoded as graphs; a lightweight parameterized quantum circuit is designed for generation, and geometric metrics—including curvature and clustering coefficient—are employed to evaluate distributional fidelity. Key contributions include: (1) the first empirical validation that QuGANs achieve high-fidelity reconstruction of core geometric distributions in route graphs while drastically reducing trainable parameters—orders of magnitude fewer than classical GANs; and (2) the discovery of a novel “high parameter efficiency–low sampling diversity” trade-off, providing both theoretical insight and empirical benchmarks for quantum generative models in geospatial data applications.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

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.

Problem

• Quantum Generative Adversarial Networks
• Hydrographic Data Processing
• Quantum Randomness for Artificial Data Generation

Innovation

• Quantum Generative Adversarial Networks
• Maritime Data Generation
• Quantum-AI Integration