💠 Quantum Bypass Framework

Bypass the classical–quantum barrier using advanced fractal–ethics mathematics.

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🚀 Overview

This repository fuses two core engines:

1. Zero Quantum Bypass – higher‑dimensional entanglement, chaos filtering, and ethical overlays.
2. Genetic Adaptation Equation – real‑time evolutionary tuning for system learning.

Together, they deliver a practical Python toolkit to simulate, test, and deploy adaptive bypass strategies that thrive even in noisy quantum‑like environments.

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📂 Project Structure

Quantum-Bypass-Framework/
├── README.md
├── LICENSE
├── requirements.txt
├── .gitignore
├── data/
│   └── sample_signals.json
├── src/
│   ├── __init__.py
│   ├── quantum_bypass.py
│   ├── equations.py
│   ├── genetic_adaptation.py
│   └── utils.py
├── tests/
│   └── test_equations.py
└── notebooks/
    └── demo_bypass_framework.ipynb

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🔬 Core Equations

$$\textbf{Entanglement Model:}\\ E(x,y,\psi) = \frac{\beta \sin(\psi x) e^{\lambda y}}{\theta x^2 + y^2} \textbf{Genetic Adaptation:}\\ G(x) = b_2 \log(b_1 + \eta Q |x|) e^{\lambda x} \bigl(1 + \alpha \delta_-(x) + \beta \delta_+(x) + \gamma e^{-\theta Q x^2}\bigr)$$

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⚡ Quickstart

pip install -r requirements.txt
python - << 'PY'
from src import QuantumBypassSystem
model = QuantumBypassSystem()
result = model.process(0.7, 1.3)
print("Bypass output:", result)
PY

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📈 Data

data/sample_signals.json contains 1,000 synthetic signal points for rapid experimentation.

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🧪 Testing

pytest tests

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✨ Contribute

PRs welcome! Align with the probability of goodness ≥ 0.9.

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📜 License

MIT