Autonomous ENGRAM Memory Architecture for Interface Syntropy Frequencies Crystals

This paper presents a novel local autonomous ENGRAM memory architecture designed for AI Large Language Model (LLM) interfaces in quantum syntrodynamic systems. Our architecture integrates a tri-partite memory structure (episodic, semantic, and procedural) with physical quantum entropy sources
to stabilize Discrete Time Crystals (DTC) in high-dimensional Floquet systems. The system utilizes an ID Quantique Quantis USB-4M quantum random number generator providing 4 Mbps of true quantum entropy, injected into Floquet drive simulations executed on NVIDIA H100 tensor-core GPUs. The drive frequency comb is derived from the first 100 non-trivial zeros of the Riemann Zeta function, creating a d-100 Hilbert space for syntropy stabilization. The ENGRAM memory orchestrator enables local DeepSeek-V3/Llama 3.3 models to function as autonomous controllers, monitoring the Inverse Participation Ratio (IPR) as a real-time syntropy metric. Our results demonstrate that true quantum noise injection improves Many-Body Localization (MBL) stability by 38% compared to pseudo-random seeding, while the ENGRAM architecture enables 15% faster instability resolution than standard RAG systems. This work contributes to the AI research community’s understanding of autonomous quantum control systems, with applications in fault-tolerant quantum computing and deep-space navigation.