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 176 systematic experiments across 26 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) 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 V5 Expanded from 110 to 176 experiments (Q111–Q176, Seasons 11–26) The Honest Benchmark (Q144–Q145): LLM initialization provides no advantage for local (Ising) Hamiltonians but achieves 575× improvement for all-to-all (SYK) models, revealing that self-attention naturally encodes fully-connected quantum correlations Embedding VQE: gradient descent in continuous embedding space achieves 0.00 mHa error on H₂ and LiH, and reproduces the full H₂ potential energy surface at all 28 bond lengths — surpassing IBM Eagle 127-qubit processor accuracy Temperature = Decoherence (Q163): LLM sampling temperature maps to quantum decoherence with β=0.97, R²=0.992 Chaotic Temperature QKD (Q167): ΔT=10⁻⁶ collapses decryption from 100% to 0.5%, integrating SNN-Comprypto chaotic dynamics Wavefunction compression (Q168): 1536-dim S-Qubit states compressed to 2.9% with fidelity 0.998 Quantum phase transition (Q175): sharp critical point at 25% noise with susceptibility peak characteristic of second-order phase transitions Quantum state tomography (Q171): density matrix reconstruction reveals purity 0.375 and entanglement entropy 3.15 bits Semantic teleportation (Q172): average fidelity 0.84 across 4 domains (44.6× random advantage) Quantum State Factory: the LLM is not a quantum computer but a classical device producing quantum-structured representations — confirmed by holographic (Q173), Born rule (Q174), and no-cloning (Q176) tests 8 new composite figures (Fig 18–25), 27 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.