⚛️ Q-Trace Pro — Quantum Python Security Analyzer Detects true quantum-native, adversarial threats in Python: probabilistic bombs, entanglement, chained logic, steganography, and quantum anti-debug. Shows real quantum risk — no classical simulation, no safe mode. 🚀 Features True quantum-native adversarial pattern detection Probabilistic bombs, chained logic, quantum steganography, entanglement, and anti-debugging logic Real quantum risk scoring — using Cirq quantum simulator (no regex, no fake simulation) Supports file uploads and direct code snippet analysis Visualizes logic extraction, risk graphs, quantum state probabilities Quantum ML anomaly detection for advanced threat identification “Red Team” adversarial code samples (optional) 🛡️ What Makes Q-Trace Pro Unique? Brutal honesty: Exposes quantum-inspired and adversarial code patterns missed by classical/static tools No safe mode: Analyses without restrictions — for advanced security and red team research No classical simulation: All patterns are mapped to quantum circuits and simulated Full logic extraction: Highlights all suspicious logic blocks, chained triggers, and cross-function threats 💡 How to Use Upload a .py Python file or paste a code snippet into the web UI Click Brutal Quantum Analysis Review detected patterns, quantum risk scores, logic extraction, and visualizations (Optional) Generate Red Team suite for adversarial sample code Requirements (for local use): Python 3.8+ cirq, numpy, streamlit, matplotlib (see requirements.txt) To run locally: bash Copy Edit pip install -r requirements.txt streamlit run app.py ⚠️ Legal / Disclaimer This tool is for defensive security research and education only. Do not use it to analyze or deploy real-world malware, ransomware, or illegal payloads. You are solely responsible for how you use this tool. 🧑💻 Credits Built by DInesh K at Voxelta Private Limited Core quantum simulation: Cirq, Streamlit, Gemini AI