This article introduces a novel architecture that combines quantum‑accelerated federated learning, multimodal retrieval‑augmented generation, and dynamic compliance knowledge graphs. It explains how the approach delivers low‑latency, privacy‑preserving evidence synthesis for real‑time regulatory compliance across distributed enterprises, while addressing scalability, security, and explainability challenges.

This article introduces a novel architecture that merges quantum‑accelerated federated learning with a self‑evolving knowledge graph. The system delivers real‑time, cross‑regulatory compliance evidence while preserving data privacy, ensuring verifiable provenance, and scaling across global enterprises.