Feature prediction in quantum graph recurrent neural networks with applications in information hiding

Abstract Graphs are fundamental structures for representing complex, nonlinear structured data across various domains, including social networks and quantum systems. Quantum graph recurrent neural networks (QGRNNs) have been proposed to model quantum dynamics in graph-based quantum systems, but their applicability to classical data remains an open problem. In this paper, we leverage QGRNNs to process classical graph-structured data. In particular, we demonstrate how QGRNN can reconstruct node features in classical datasets. Our results show that QGRNN achieves high feature reconstruction accuracy, resulting in near-perfect classification. Furthermore, we propose an information hiding technique based on our QGRNN, where messages are embedded into a graph and then retrieved under certain conditions. We assess retrieval accuracy for different dictionary sizes and message lengths, showing that QGRNN maintains high retrieval accuracy, with minor degradation as complexity increases. These findings demonstrate the scalability and robustness of QGRNNs for both classical data processing and secure information hiding, paving the way toward quantum-enhanced feature extraction, privacy-preserving computations, and quantum steganography.