Bibliographic record
Abstract
This side paper presents a full hardware benchmark of Quantum Information Refinement Theory (QIRT) version 6, QIRT Meta-Floor Protocol applied to the NP-hard 3-Exact Cover problem. The QIRT v6 architecture implements Adaptive Quantum Error Correction (QEC) Floor-Shaping to guide symbolic collapse toward valid clause solutions without Grover’s search or QUBO penalty functions. Benchmarks were executed on the 127-qubit IBM Sherbrooke quantum processor in July 2025, achieving a mean Clause Satisfaction Rate (CSR) of 6.13% over 26,240 shots—more than doubling the standard baseline for this problem class. All circuits, raw outputs, and analysis methods are provided for reproducibility. This report supplements the main QIRT7 white paper and demonstrates the practical utility of entropic shaping and meta-floor QEC in symbolic clause-solving tasks. For full theoretical background and additional benchmarks, see: [link to main QIRT7 Zenodo/arXiv page]. Keywords: QIRT, quantum computing, 3-Exact Cover, IBM Sherbrooke, symbolic collapse, quantum error correction, entropic shaping, hardware benchmark, clause satisfaction, quantum information refinement All content is protected under US Provisional Patent 63/826,473. No use in commercial or institutional projects is authorized without permission. Publicly released for research transparency only.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".