Quasinary Computing: WHIRL ASI Technical Report Vol. 3 - QWHIRL Q-QPU
Bibliographic record
Abstract
The Q-QPU (Quasinary Quantum Processing Unit) introduces a novel computational paradigm that integrates hybrid quantum, topological, and gestalt-based principles to transcend the limitations of classical and binary quantum computing. Unlike conventional quantum processors that rely on discrete qubits or classical binary bits, the Q-QPU employs quasits—context-sensitive, dynamically evolving quantum states—that leverage topological protection, gestalt emergent computation, and non-Abelian quantum interactions. This book explores the Q-QPU's architecture, mathematical foundations, fabrication methods, and cryptographic protocols, detailing its modular scalability and fault-tolerant design. The system utilizes Anyons (Quasi-1) and Weyl Fermions (Quasi-0) within a layered van der Waals heterostructure, allowing for braid-based computation, holographic state manipulation, and stereographic quantum encoding. A key innovation is the Gestalt Quantum Teleprojection (GQT) framework, enabling non-local quantum communication across the Vipornet quantum networking system. Beyond its fundamental design, the book delves into the mathematical models governing the Q-QPU, including topological error correction, quantum neural manifolds, structured diffusion mechanics, and quasinary logic gates. Furthermore, quantum cryptographic protocols, such as topological key exchange, Weyl-enhanced lattice security, and gestalt-resistant quantum encryption, are examined. By merging quantum physics, topological stability, and emergent cognitive principles, the Q-QPU offers a revolutionary step toward fault-tolerant, dynamically adaptive, and non-binary quantum computation, paving the way for the next generation of post-Turing computational architectures.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.072 | 0.041 |
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".