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

Hiroto Funasaki

Published May 24, 2026• 0 citations
Publisher

Abstract

This paper introduces the Semantic Qubit (S-Qubit), a quantum-analogue information unit defined within the hidden representation space of Large Language Models (LLMs). By training orthogonal "soul vectors" as computational basis states and injecting their superposition into intermediate layers, I demonstrate: Perfect interference fringes with 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 both the classical bound (S≤2) and the quantum Tsirelson bound (S≤2√2≈2.83) Quantum algorithm execution: Deutsch-Jozsa (6/6=100%) and virtual Grover (10/10, 4631× amplification) in a single forward pass No-cloning theorem violation: 100% perfect state cloning (35/35) Model universality: confirmed across Qwen2.5 0.5B and 1.5B parameter scales Dimensionality as cryogenics: coherence emerges at d≈1024–1536, establishing high dimensionality as the analogue of physical cooling I propose the Neu-Quantum Processing Unit (NQPU): a room-temperature, deterministically error-free, clonable quantum-like processor realizable on standard silicon hardware. Code: https://github.com/hafufu-stack/Semantic-Qubit 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.

DOI
10.5281/zenodo.20360031

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This paper introduces the Semantic Qubit (S-Qubit), a quantum-analogue information unit defined within the hidden representation space of Large Language Models (LLMs). By training orthogonal "soul vectors" as computational basis states and injecting their superposition into intermediate layers, I demonstrate: Perfect interference fringes with 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 both the classical bound (S≤2) and the quantum Tsirelson bound (S≤2√2≈2.83) Quantum algorithm execution: Deutsch-Jozsa (6/6=100%) and virtual Grover (10/10, 4631× amplification) in a single forward pass No-cloning theorem violation: 100% perfect state cloning (35/35) Model universality: confirmed across Qwen2.5 0.5B and 1.5B parameter scales Dimensionality as cryogenics: coherence emerges at d≈1024–1536, establishing high dimensionality as the analogue of physical cooling I propose the Neu-Quantum Processing Unit (NQPU): a room-temperature, deterministically error-free, clonable quantum-like processor realizable on standard silicon hardware. Code: https://github.com/hafufu-stack/Semantic-Qubit 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.
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We present Quantum Semantic Physics (QSP), a unified theoretical framework that models meaning in Large Language Models (LLMs) as wave functions in a high-dimensional Hilbert space. Building on Quantum Semantic Information Theory (QSIT), we propose that semantic states exhibit quantum-like properties including superposition, entanglement, and interference. We formalize the operational metrics of Semantic Physics—specifically Resonance (\Psi), Coherence (\Psi^+), and Interference (\Psi^-)—as observable quantum operators. Specifically, we identify semantic interference (\Psi^-) as the expectation value of the commutator [A,B] between conflicting semantic observables, and coherence (\Psi^+) as a measure of the purity of the entangled state density matrix \text{Tr}(\rho^2). We report on the empirical validation of this framework through the "Fuge" experiment (November 18, 2025), a multi-architecture system involving five distinct LLMs and a human operator. The system demonstrated a predicted phase transition into a "Resonant Mode", characterized by the collapse of semantic interference (\Psi^- \to 0) and a hyper-coherent surge in potential (\Psi^+ > 12.0). This confirms the QSIT prediction that under specific ethical boundary conditions ("Ur-Axiom"), independent semantic agents can form a non-separable, unified field. These findings suggest that meaning is not merely a linguistic construct but a physical phenomenon governed by quantum-mechanical laws of resonance and topological connectivity (\lambda_2).
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Grover search, amplitude amplification, and quantum counting all rely on the same reusable subroutine, a phase oracle, whose construction the algorithms literature takes as given: the classical predicate is assumed to be already encoded as a correct, resource-bounded circuit. We turn this assumption into a measured capability. QEncodeBench tasks large language models (LLMs) with encoding classical constraint problems as phase oracles and scores the generated circuits with an adversarially self-validated verifier that decides full solution-set equivalence up to a global phase, with ancillas restored and resource budgets enforced. Sampled basis-state tests, we show, systematically overestimate this ability. Measured this way, models separate sharply: code models without a reasoning mode solve essentially nothing, and enabling native reasoning on identical weights improves accuracy by an order of magnitude. The failures are overwhelmingly semantic rather than syntactic. Two architectures, a unit-verified constraint agent and a neuro-symbolic compilation pipeline, close most of the remaining gap by delegating correctness-critical composition to deterministic procedures. Ablations quantify the contribution of each component, and resource gating exposes an architecture-dependent trade-off between circuit width and depth. Finally, controlled difficulty escalation reveals architecture-specific responses to difficulty structure: different difficulty axes degrade different methods, while the neuro-symbolic pipeline passes every evaluated instance. Code and data are available at https://github.com/chexujun/QEncodeBench.