Standard autoregressive Large Language Models (LLMs) suffer from three foundational bottlenecks across both enterprise cloud clusters and resource-constrained edge devices: Catastrophic Forgetting during continual weight updates, High Decision Latency (15–45 seconds) caused by sequential Chain-of-Thought token generation, and Probabilistic Hallucination when enforcing strict logical axioms. In this whitepaper, we introduce Q-PHC v2.0 (Quantum-Inspired Priya Hybrid Cognitive Brain), a universal dual-process neuro-symbolic cognitive architecture capable of scaling from frontier cloud clusters (10^9 rules) down to a strict 4.0 GB RAM edge envelope on commodity CPU silicon. Rather than simulating computationally prohibitive quantum gates (O(m · N) overhead), Q-PHC v2.0 leverages algebraic Hyperdimensional Computing (HDC) and hardware-accelerated SIMD bitwise routing. The architecture unifies four core pillars: (1) a permanently frozen Neural Perceptual Core (scaling from 1B edge SLMs to 400B+ frontier models), (2) a sub-4ms Binary Entangled Router, (3) an out-of-core Deterministic Rule Vault formally verified by an SMT Theorem Prover, and (4) a continual grammar-constrained ingestion pipeline protected by a Ring-0/1/2 cryptographic security shield. By compiling slow neural deliberation into permanent "Habit Reflexes", Q-PHC v2.0 achieves deterministic execution latencies of 2.5 to 7.0 ms, reduces enterprise GPU inference costs by up to 90%, and guarantees 0.0% catastrophic forgetting.
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Aman Bindra (2026) studied this question.
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