Quantum Communication Networks for Secure Foundation Model Distribution Across Shanxi Power Grid Substations

INTRODUCTION: Foundation models deployed in power grid infrastructure face escalating security threats from advancing quantum computing capabilities, which can compromise classical encryption protecting multi-gigabyte model parameters during distributed transmission and storage across geographically dispersed substations. OBJECTIVES: This research develops and validates a quantum-secured communication framework integrating quantum key distribution with hierarchical encryption mechanisms specifically designed for protecting foundation model parameters in operational power grid environments. METHODS: A dual-channel quantum communication architecture was deployed across five substations in Shanxi Province spanning 580 kilometers, implementing BB84 protocol with decoy state techniques for quantum key generation, dynamic key-data mapping algorithms for parameter encryption, and Ceph-based distributed storage with blockchain audit trails. The system underwent 30-day continuous operational validation protecting a 500-million-parameter Transformer model under real-world conditions including temperature variations (-5°C to 35°C), grid maintenance events, and concurrent SCADA traffic. RESULTS: The framework achieved 99.2% system availability with distance-dependent quantum key distribution rates ranging from 4.5 kbps (50 km) to 0.5 kbps (180 km), quantum bit error rates maintained between 3.2-11.4% within operational thresholds, hierarchical AES encryption throughput of 85 MB/s for model parameters, and storage system performance delivering 8,500-10,800 read IOPS with 1.05 ms average latency. CONCLUSION: This work validates the practical viability of quantum communication networks for securing distributed foundation models in critical power infrastructure, demonstrating information-theoretic security under operational network conditions while establishing integration protocols between quantum key distribution channels and encrypted data transmission pathways for large-scale AI model protection.