Quantum Assisted Flight Path Optimization

Modern aviation faces increasing operational complexity due to rising air traffic, fluctuating fuel costs, dynamic weather patterns, and stringent safety and environmental regulations. Traditional flight planning methods are often insufficient to handle the scale and variability of modern aviation. This paper proposes a Django-based backend platform that integrates classical algorithms, quantum-inspired optimization, real-time data analytics, and artificial intelligence for efficient flight planning and operational support. The system consolidates airport data, route optimization using QAOA-inspired algorithms, weather analytics, fuel and emissions estimation, and safety metrics into a unified API. To improve usability, a chatbot interface powered by Google Gemini provides natural language interaction. Results demonstrate improved operational efficiency, explainability, and robustness, highlighting the potential of AI and quantum-assisted methods for enhancing aviation safety and sustainability.