Quantum Assisted Flight Path Optimization

Abstract

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.

Introduction

Modern aviation is increasingly complex due to rising traffic, volatile fuel prices, dynamic weather, and strict regulations. Traditional flight planning methods are no longer sufficient. This system addresses these issues using a hybrid approach combining:

• Classical algorithms

• Quantum-inspired optimization (QAOA)

• Real-time data analytics

• AI-powered chatbot (Google Gemini)

???? Key Features

1. Route Optimization – Using classical (Dijkstra) and QAOA-inspired methods.

2. Weather Analytics – Real-time and forecast-based via APIs.

3. Fuel & Emissions Estimation – Based on Airbus A380 performance data.

4. Safety Metrics – Derived from telemetry, simulations, and heuristics.

5. Conversational Interface – Google Gemini-powered chatbot for interaction and explanations.

???? Literature Foundation

• Rooted in operations research, aerospace, and AI.

• Uses classical algorithms (e.g., Dijkstra) and modern methods (QAOA, ML).

• Data sources: NASA, OpenSky, Open-Meteo, and quantum research studies.

?? System Architecture

A modular Django backend with REST APIs and a layered design:

1. Input Layer – User inputs (origin, destination, preferences).

2. Data Layer – Static & dynamic data handling with caching.

3. Optimization Engine – Combines classical and quantum-inspired algorithms.

4. Analytics Module – Fuel, cost, emissions, safety metrics.

5. API Layer – Endpoints for search, optimization, reports, and chatbot.

6. Chatbot Interface – For natural language queries and insights.

7. Output Layer – Displays routes and metrics in dashboards/chatbot.

???? Technologies Used

• Languages & Frameworks: Python 3.10+, Django REST, TensorFlow/Keras

• APIs: OpenSky, Open-Meteo, Google Gemini

• Algorithms: Dijkstra, QAOA-inspired transforms, fallback algorithms

? Functional Highlights

• Airport search, route optimization, full report generation

• Deterministic fallbacks for API failures

• High performance (<500ms response) and scalable design

???? Results

• QAOA-ML hybrid offered better trade-offs between fuel and safety.

• Deterministic fallbacks improved system robustness.

• Explainable insights via the integrated chatbot.

???? Comparison with Traditional Methods

References

[1] NASA Glenn Research Center, “Lift-to-Drag Ratio,” [Online]. Available: https://www.grc.nasa.gov/www/k-12/airplane/lift_drag.html [2] NASA Glenn Research Center, “Drag Equation,” [Online]. Available: https://www.grc.nasa.gov/www/k12/airplane/drageq.html [3] NASA Glenn Research Center, “Thrust Equation,” [Online]. Available: https://www.grc.nasa.gov/www/k-12/airplane/thrsteq.html [4] NASA Technical Reports Server, “Guidance, Flight Mechanics and Trajectory Optimization,” [Online]. Available: https://ntrs.nasa.gov [5] Y. Li et al., “Research on Flight Trajectory Optimization Based on Quantum Genetic Algorithm,” Journal of Physics: Conference Series, 2020. [6] OpenSky Network, “OpenSky Network API Documentation,” 2025. [7] Open-Meteo, “Open-Meteo Weather API Documentation,” 2025. [8] P. Givi et al., “Quantum Speedup for Aeroscience and Engineering,” AIAA Journal, 2020. [9] H. Makhanov et al., “Quantum Computing Application for Flight Trajectory Optimization,” arXiv preprint, 2023. [10] N. Innan et al., “QUAV: Quantum-Assisted Path Planning and Optimization for UAV Navigation,” arXiv preprint, 2025. [11] R. Haba et al., “Routing and Scheduling Optimization for Urban Air Mobility Fleet Management using Quantum Annealing,” Scientific Reports, 2025. [12] M. Gili et al., “Optimization of Flight Routes: QAOA for Tail Assignment Problem,” arXiv preprint, 2024.

Copyright

Copyright © 2025 Anusha K. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.