Quantum computing is emerging as a potentially transformative technology for air traffic management, where safe separation, fuel efficiency and real-time adaptability create highly complex optimisation challenges. Even limited traffic volumes can generate decision spaces that are computationally intractable with conventional methods. Building on earlier work using explainable artificial intelligence and evolutionary algorithms for conflict resolution, NEXT-QCM explores how quantum optimisation and quantum machine learning can enable scalable, real-time trajectory optimisation. 

Building on the ARTIMATION project, which applied explainable AI and genetic algorithms for pairwise conflict resolution, NEXT-QCM will explore the role of quantum computing in enabling scalable, real-time trajectory optimisation. The framework targets global optimisation across sectors or networks, real-time adaptation to changing conditions, and improved computational efficiency through quantum-enhanced learning, while maintaining transparency through explainable methods. By applying advanced encoding, noise mitigation and validation on realistic field cases, NEXT-QCM demonstrates the potential of quantum computing to support more efficient, sustainable and resilient airspace management.