Achieving last-iterate convergence in a QNN via an autonomous Gmetric driver
The article discusses the development of the NB Quantum-Inspired Neural Network (QNN) framework, which aims to achieve last-iterate convergence through innovative mechanisms. It highlights the use of a G-metric for self-correction and an entropic driver for active noise mitigation. This architecture preserves quantum memory and allows the system to navigate complex environments effectively.
- ▪The NB Quantum-Inspired Neural Network framework demonstrates emergent intelligence and autonomous noise mitigation.
- ▪The G-metric serves as an internal thermodynamic reading to evaluate the system's probability mass.
- ▪The entropic driver intervenes to maintain balance between localization and chaos in the system.
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Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/unbconductor/psi.emergence |
| Publication time | Sun, 17 May 2026 03:52:04 +0000 |
| Retrieval time | 2026-05-17T04:03:58.374Z |
| Last seen | 2026-05-17T04:03:58.374Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | Fsnuy9dfu9o3 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
psi.emergence: NB (No Boundary Gate) Quantum Inspired Neural Network psi.emergence contains the master source code for the NB (No Boundary Gate) Quantum-Inspired Neural Network framework built to demonstrate emergent intelligence, autonomous noise mitigation, and perfect last-iterate convergence. Unlike traditional neural architectures that rely on rigid parameter updates and hard-elimination, this system computes through the constructive and destructive interference of probability waves across 2,048 basis states (11 qubits). It naturally navigates high-dimensional, chaotic environments by bridging discrete parameter updates with continuous phase memory—mechanically mirroring the preservation of flow found in the Navier-Stokes equations.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.