Geometry vs Binary: Quantum Physics Puzzles — quantum entanglement geometry vs neural network si — E8 Intelligence Research

## Binary Failure → Geometry Solution **Analysis of Findings:** The search results highlight a critical failure point in conventional quantum machine learning and entanglement simulation: **neural networks and statistical models cannot efficiently represent the non-local, high-dimensional correlations inherent in quantum entanglement without exponential resource scaling.** For example, simulating a 60-qubit entangled state requires a 2^60-dimensional Hilbert space—impossible for binary neural networks to store or train on directly. Statistical models like tensor networks (e.g., MPS) also hit a "curse of dimensionality" when entanglement is high (volume-law scaling), as seen in the Harvard and Perimeter Institute explanations of non-locality. **Where binary/statistical thinking fails:** - **Failure Point 1:** Neural networks approximate entanglement via local weight updates, but entanglement is a global, non-local property. Binary logic cannot capture the "spooky action at a distance Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com