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Explainable Quantum Machine Learning Concepts for Trajectory Optimization in Air Traffic Management

Authors:

Shahina Begum, Shaibal Barua, Mobyen Uddin Ahmed, Henri de Boutray , Christophe Hurter

Research group:

Artificial Intelligence and Intelligent Systems

Publication Type:

Conference/Workshop Paper

Venue:

International Conference on Modern Artificial Intelligence and Data Science Systems

Abstract

Quantum computing offers a transformative paradigm for solving computationally intractable problems by leveraging quantum-mechanical principles such as superposition and entanglement. In Air Traffic Management (ATM), this capability has the potential to revolutionize trajectory optimization and airspace configuration, which are NP-hard problems requiring real-time, safety-critical decisions. Unlike classical algorithms that search sequentially, quantum optimization routines can evaluate exponentially large sets of trajectory combinations in parallel, providing a spectrum of near-optimal solutions that adapt dynamically to operational changes such as weather or emergency events. This study explores concepts of how Quantum machine learning (QML) with explanation can extend these methods to multi-aircraft, large-scale scenarios. The proposed conceptual framework identifies four key focus areas: (1) Comprehensive global optimization across sectors and net-works; (2) Real-time adaptability through continuous quantum-enhanced re-evaluation; (3) Quantum-enhanced optimization, integrating Quantum Machine Learning (QML) for superior scalability and performance; and (4) Explainable Quantum Machine Learning (XQML), advancing interpretability and trust in AI-assisted decision support. It conceptually illustrates how the integration of quantum optimization, scalable QML techniques, and explainable AI could significantly enhance the efficiency and trustworthiness of trajectory optimization in future ATM systems.

Bibtex

@inproceedings{Begum7312,
author = {Shahina Begum and Shaibal Barua and Mobyen Uddin Ahmed and Henri de Boutray and Christophe Hurter},
title = {Explainable Quantum Machine Learning Concepts for Trajectory Optimization in Air Traffic Management},
month = {May},
year = {2026},
booktitle = {International Conference on Modern Artificial Intelligence and Data Science Systems},
url = {http://www.es.mdu.se/publications/7312-}
}