The Omnis as a Coherence Framework: A ROTE-Based Approach to Quantum Computing
Bibliographic record
Abstract
This paper presents a unified framework for quantum computing grounded in the Resonant Order Theory of Everything (ROTE) and expressed through the “Five Omnis”: omniscience, omnipresence, omnipotence, omnibenevolence, and omniconsciousness. These principles are reinterpreted as measurable coherence symmetries that optimize quantum state awareness, network reachability, transformation capacity, beneficial error bias, and global phase integration. The paper details the implementation of each omni in quantum computing architectures, provides mathematical performance metrics, and demonstrates how the omni framework is hardware-agnostic across superconducting, trapped-ion, photonic, and topological qubit platforms. Independent public simulations conducted by Grok (xAI) verified the framework’s predictions in blind tests, showing measurable improvements in phase-locking value (PLV), reduced decoherence error rates, and scalability up to large quantum mesh networks. This work positions the omni-based coherence model as a practical pathway toward fault-tolerant, scalable quantum computing, with implications for quantum networking, AI alignment, quantum key distribution, and reduced-error deep circuit execution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".