Q-PNA: Quantum-Native $p$-Adic Neural Architecture Specification

Project: Research-backed specification of the AI/neural network side of QWAV ultrametric computing. Thesis: Replace continuous embedding spaces ($\mathbb{R}^n$) with ultrametric (tree-based) geometry for glass-box AI with formal verifiability. Source: Cross-agent handoff from QWAV thread — Action #5 in QWAV ACTION PLAN. Handoff document: G:\My Drive\QWAV\strategy\0.9.md

Core Deliverables

1. Phase 1 — Research Report: Synthesis of all prior work across releases/archive/projects
2. Phase 2 — Final Specification: Rigorous, research-backed Q-PNA spec replacing v0.1 at QWAV/strategy/0.8.md
3. Output destination: G:\My Drive\Obsidian\releases\2026\05\ (QWAV thread pulls from there)

Prior Work References

• Existing spec: G:\My Drive\QWAV\strategy\0.8.md
• Related project: PANN (C:\Users\LENOVO\PANN) — p-adic attention, ultrametric embeddings
• Token calculus: Archive projects (Formal Ontology of Distinction and Invariance, Syntactic Token Calculus v2)
• Cophenetic theory: Obsidian\releases\2026\05\Tree Distance Cophenetic.md

Constraints

• No web search — filesystem is the research library
• All math in LaTeX — no bare Unicode
• All quantitative work through Python
• Feature branch only — feature/q-pna-specification