Omniversal Quantum Genesis Orchestration
Bibliographic record
Abstract
The Omniversal Quantum Genesis Orchestration (OQGO) — also referred to as the Omniverse Quantum Gravitational Observatory — is a pioneering quantum computing program developed by Dr. Zuhair Ahmed at the Centre of Excellence for Technology Quantum and AI Canada (CETQAP). Using Qiskit, OQGO simulates and analyzes quantum systems with applications in fundamental physics. By leveraging 10 qubits in its core experiments — with extended testing on 133-qubit systems like IBM Brisbane, Sherbrooke, and Torino — OQGO aims to replicate behaviors observed in high-energy physics experiments, such as those at the Large Hadron Collider (LHC), as well as astrophysical phenomena like gravitational waves detected by LIGO. This article explores OQGO’s methodology, mathematical framework, results across multiple runs, and its potential to bridge quantum mechanics and gravity, offering insights into a unified “Theory of Everything.” With modest computational resources but ambitious goals, OQGO represents a bold step in quantum simulation for cosmic exploration. The source code and further details are available at: https://github.com/CETQAP/OQGOThe datasets generated and analyzed during this study are available in the OQGO repository at the same link.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".