Q-Optima - Autonomous Quantum Logistics Agent

Q-Optima is a production-ready multi-cloud autonomous AI agent that solves the Travelling Salesman Problem an NP-Hard logistics optimization challenge using real Quantum Computing. HOW IT WORKS: A logistics commander speaks a voice instruction and uploads a delivery route map. The 7-node LangGraph agent pipeline: 1. Speechmatics (sponsor) transcribes the audio instruction in real time 2. Gemini 2.5 Flash Lite analyzes the map image and extracts city locations 3. OSRM, Open-Meteo, TomTom, and Climatiq enrich the distance matrix with real road distances, weather penalties, traffic congestion, and carbon costs 4. The agent auto-generates a Qiskit QAOA circuit from the enriched QUBO matrix 5. IBM Quantum (or Aer simulator) executes the circuit with 512 shots 6. A self-reflection loop autonomously retries with higher QAOA depth if quality fails 7. The optimal route is decoded, a Telegram dispatch alert fires, and the job is saved to Supabase analytics WHAT MAKES IT ORIGINAL: - First agent combining multimodal vision AI + real quantum computing for logistics - QUBO matrix encodes weather + traffic + carbon simultaneously — one quantum pass - LangGraph self-reflection: the agent improves its own answer autonomously - Classical vs quantum comparison shows measurable advantage TECH STACK (14 APIs, 3 clouds, $0 cost): Frontend: Next.js 14, Framer Motion, Leaflet.js — Vercel Backend: FastAPI, LangGraph, Python 3.11 — Render STT: Speechmatics batch + RT API (sponsor) Vision: Google Gemini 2.5 Flash Lite Quantum: IBM Quantum / Qiskit QAOA GPU STT: OpenAI Whisper on AMD ROCm Road data: OSRM (free, no key) Weather: Open-Meteo (free, no key) Traffic: TomTom Flow API Carbon: Climatiq freight emissions Dispatch: Telegram Bot API Analytics: Supabase PostgreSQL Tunneling: Cloudflare Tunnel