Semantic-Qubit: Discovering Universal Quantum-Like Coherence in Large Language Models

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 52 systematic experiments 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: O(1) scaling — 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%) Superdense coding: 200/200=100% at 2.0 bits per S-Qubit 128× quantum parallelism: one forward pass encodes 7 classical bits of information No-cloning violation: 100% perfect state cloning (35/35) Model universality: confirmed across Qwen2.5 0.5B, 1.5B, and 3B parameter scales 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 7 benchmark algorithms is 74.6/100. Code: https://github.com/hafufu-stack/Semantic-Qubit What's New in V2 Expanded from 24 to 52 experiments (Q25–Q50) Three new quantum oracle algorithms with 100% accuracy (Bernstein-Vazirani, Simon's, Deutsch-Jozsa) Complete BB84 quantum key distribution protocol with eavesdropper detection Superdense coding: 2.0 bits per S-Qubit (200/200 = 100%) 128× quantum parallelism benchmark Grand Unified Benchmark: Quantum Advantage Score = 74.6/100 Failure analysis: cross-layer teleportation, GHZ states, and QAOA limitations clarified Refined theoretical framework: "Dimensionality provides interference; training provides entanglement" 3 new figures (Fig 7–9), 4 new references, 11 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.