Hybrid Neural Quantum Architecture (HNQA): Toward Probabilistic Cognition in Deterministic Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This white paper introduces the Hybrid Neural Quantum Architecture (HNQA) — a theoretical framework that integrates deterministic neural learning with quantum-inspired probabilistic state encoding. HNQA proposes a dual-layer system: a classical deterministic core coupled with a probabilistic amplitude layer capable of representing multiple potential states simultaneously. The architecture models cognitive processes such as perceptual ambiguity and contextual collapse, aiming to enable artificial systems that learn from uncertainty rather than minimizing it.The document outlines the conceptual structure, mathematical rationale, and potential applications across adaptive AI governance, cybersecurity, cognitive simulation, and hybrid computation. It further discusses implementation challenges, energy efficiency, and integration paths with existing deep-learning frameworks.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it