SOURCE-LINKED INTELLIGENCE

TOWARDS EXPLAINABLE QUANTUM AI SOLUTIONS FOR NEXT -GENERATION TRAJECTORY OPTIMISATION

CORDIS · observation · Publication date unknown

wise conflict resolution, this study explores the role of quantum computing in enabling scalable, real-time trajectory optimisation. The NEXT-QCM framework integrates quantum optimisation and Quantum Machine Learning (QML) to evaluate exponentially large sets of routing alternatives in parallel, offering conflict-free, fuel-efficient, and sustainable solutions. Four core innovations underpin the approach: (1) global optimisation across entire sectors or networks, balancing safety, efficiency, and environmental impact; (2) real-time adaptability, using quantum simulators such as ColibrITD’s MPQP to reconfigure trajectories as new data arrive; (3) quantum-enhanced optimisation via QML to accelerate model learning and improve computational efficiency; and (4) explainable QML (XQML) to enhance interpretability and build controller trust. By leveraging advanced encoding, noise mitigation, and error-correction techniques, NEXT-QCM aims to deliver a proof-of-concept system validated through realistic field cases. This exploratory research demonstrates how quantum computing can move A

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recordType

award

status

SIGNED

region

EU

value

999997

unit

EUR