Geometry vs Binary: Quantum Physics Puzzles — quantum geometry problem unsolved by machine learn — E8 Intelligence Research
Andrew Stewart Caldin
## Binary Failure → Geometry Solution **Analysis of Findings:** The search results reveal a critical gap: quantum machine learning (QML) researchers are *borrowing* geometric ideas (e.g., Cerezo's "Geometric Quantum Machine Learning") but still framing them as **optimization problems over parameter spaces** — a fundamentally statistical approach. The "unsolved geometry problem" talk (Lost in Forest) is about classical olympiad geometry, not quantum. The real failure is that **no one connects the discrete, non-commutative structure of quantum states to a fixed lattice geometry** — they treat quantum states as continuous vectors in Hilbert space, then apply neural nets to *approximate* correlations. **Where binary/statistical approaches hit a wall:** 1. **Quantum entanglement classification** — Neural networks classify entangled vs. separable states by training on millions of examples. But entanglement classes (GHZ, W, cluster, etc.) are **discrete, topologically distinct orbits** un Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com
•Oct 13, 2026•0 citations