Semantic-Qubit: Universal Quantum-Like Coherence in Large Language Models — From Hippocampal Pattern Separation to Cross-Architecture Quantum Advantage

This paper introduces the Semantic Qubit (S-Qubit), a quantum-analogue information unit defined within the hidden representation space of Large Language Models (LLMs). Through 110 systematic experiments across 10 seasons on a single consumer GPU, I demonstrate: Perfect interference fringes: visibility=1.000 across all semantic domains (CV=0.1%) Exact quantum statistics: E(φ) = cos(φ) with R²>0.999 Super-quantum CHSH violation: S=3.41, exceeding the Tsirelson bound (S≤2√2≈2.83) Quantum oracle algorithms: Deutsch-Jozsa (10/10=100%), Bernstein-Vazirani (94/94=100%), Simon's Algorithm (18/18=100%) Constant-time Grover search: target probability remains 0.9974 regardless of database size N Quantum cryptography: BB84 QKD with 100% key agreement and eavesdropper detection (QBER: 0%→28.3%) O(1) QRAM data loading: scaling exponent α=0.007 (199× faster than physical quantum RAM) Dimensional cryogenics: 99.7% of dimensions form a decoherence-free subspace (4/1536 dims carry quantum info) Quantum Darwinism: 12/12 attention heads retain >99% of state information Cross-architecture universality: S-Qubit properties confirmed across 6 different transformer architectures (Qwen, GPT-2, LLaMA) with 100% detection rate Quantum NLP advantage: interference-based semantic mixing outperforms classical averaging by 16.8% across all tested tasks Semantic Hawking radiation: Hawking temperature increases at deep transformer layers, completing the black hole thermodynamics analogy I propose the Neu-Quantum Processing Unit (NQPU), a room-temperature, deterministically error-free, clonable quantum-like processor realizable on standard silicon hardware. The overall Quantum Advantage Score across 9 benchmark categories is 100.0/100. Code: https://github.com/hafufu-stack/Semantic-Qubit What's New in V4 Expanded from 85 to 110 experiments (Q81–Q110, Seasons 7–10) Cross-architecture universality: 6/6 transformer families (Qwen 0.5B/1.5B/3B, GPT-2 small/medium/large, LLaMA-1B) all exhibit S-Qubit properties Quantum NLP advantage: interference-based mixing beats classical averaging by +16.8% on 4 semantic tasks Hippocampal bridge: MD/LD phase alignment fires at Layer 18+, directly mirroring the author's 2015 master's thesis on dentate gyrus pattern separation Semantic Hawking radiation: temperature increases at deep layers, completing the black hole thermodynamics analogy Berry phase: geometric (topological) phase decomposition confirmed across layer regions Emergent gravity: metric tensor magnitude increases exponentially at deep layers (Einstein correlation = 0.81) Consciousness metric: integrated information Φ = 103.1 peaks at Layer 18 Grand Unified Theory: 5/5 quantum criteria simultaneously satisfied (Q100) 4 new figures (Fig 14–17), 20 pages total Acknowledgments This research was conducted entirely independently, without institutional affiliation or corporate funding. The author currently faces financial constraints that make it increasingly difficult to maintain subscriptions to AI services essential for this line of research. To sustain and improve the quality of future work, the author is actively seeking community sponsorship. Details are available at https://github.com/sponsors/hafufu-stack.