Supply chain resilience modeling based on dynamic hypergraph and quantum reinforcement learning for low-altitude-ground networks

This study proposes an innovative resilience optimization framework integrating dynamic hypergraph theory and quantum reinforcement learning to address the unique structural characteristics and vulnerabilities of low-altitude economic supply chain networks. By incorporating multi-source supply chain data, we construct a dynamic hypergraph model based on Spearman rank correlation, revealing the hub-and-spoke topological features of low-altitude supply networks. Utilizing quantum state encoding and entanglement gate optimization techniques, we develop a quantum reinforcement learning algorithm with stable convergence properties for real-time optimization in high-dimensional decision spaces. Furthermore, we establish a quantum-inspired anomaly detection system that effectively identifies systemic risks through spectral analysis and multivariate statistical process control. Model validation results confirm the framework’s capability to accurately capture seasonal fluctuation patterns in low-altitude supply chains and provide early warnings for critical infrastructure nodes. The proposed approach significantly reduces seasonal disruption durations while avoiding off-peak resource redundancy through strategic inventory buffering of key hub nodes and dynamic supplier adjustments. The research contributes three key aspects to low-altitude supply chain management: (1) topology-aware planning methods based on hypergraph centrality metrics, (2) quantum adaptive optimization strategies incorporating temporal patterns, and (3) proactive risk management systems driven by quantum spectral analysis. This work not only provides novel management tools for emerging low-altitude economic systems but also opens new research pathways for resilience optimization in complex supply chain networks.