QIRT Benzene Ring Hardware Benchmark Dataset (IBM Sherbrooke, July 2025)
Bibliographic record
Abstract
This dataset provides the raw hardware outputs, circuit details, and analysis results from experimental demonstrations of Quantum Information Refinement Theory (QIRT) applied to a 6-qubit benzene ring model, executed on the IBM Sherbrooke quantum processor in July 2025. QIRT introduces a new class of quantum algorithms designed to preserve and refine quantum delocalization, coherence, and symmetry beyond what is achievable with standard VQE or chemistry methods. The dataset includes: The exact OpenQASM circuit (MOLE.qasm) used for the experiment. Raw job metadata and bitstring result files from the IBM QPU. A grouped symmetry-class CSV and supporting Python scripts for reproducibility and entropy analysis. Device calibration files for full auditability. Key results include a symmetry entropy of 5.51 bits (out of 5.93 maximum), confirming near-ideal quantum delocalization and ring symmetry in hardware—an unprecedented achievement in quantum chemistry simulation. All data is released for scientific benchmarking and transparency under the Creative Commons Attribution 4.0 International License.The underlying QIRT method, algorithms, and software are protected by US Provisional Patent Application 63/826,473 and the Business Source License 1.1 (BSL 1.1).Commercial or research integration of QIRT algorithms, code, or firmware is strictly prohibited without written permission.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.039 |
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".