This work bridges quantum optics and neuromorphic engineering to provide a 13-step computational proof-of-concept for Phase-Dependent Collective Contrast. We demonstrate that the 1. 91x Critical Scaling Gap—originally identified in the dissipative Dicke model—serves as a robust stability anchor for engineering ultra-sparse, "Elite" spiking neural networks. Using the Nengo neuromorphic framework, we translate quantum steady-state ratios into a neural "Refusal Ratio" (R₄₅₅), achieving stable binding attractors at an ultra-low firing rate of 0. 0417 spikes/ms. This repository contains the complete "Hu Tao" protocol, ranging from QuTiP-based quantum simulations to autonomous neuromorphic motor control (Ping Pong) and quantum-seeded neural crossover. Key Highlights: Theoretical Foundation: Grounded in the Phase-Dependent Scaling Program (2026) and the gc = 6/pi convergence law. Neuromorphic Implementation: Successful mapping of the 1. 912601 stability constant to neural refusal gates. Data Included: 13 sequential Jupyter Notebooks documenting the full discovery pipeline from "Duel" simulations to "Quantum-Seeded Birth. " Related Works: This manuscript is theoretically grounded in the 1. 91x Critical Scaling Gap identified in Scaling Behavior in Noisy Dicke Model Simulations (Omandac 2026, Paper 1).
Omandac Clarence (Tue,) studied this question.