ML-QTrack: Learning-Guided Global Optimization for Full-Detector Particle Tracking

Charged-particle track reconstruction is a fundamental yet challenging problem in high-energy physics, with full-detector tracking posing a particularly difficult trade-off between reconstruction quality and computational cost. Learning-based methods can efficiently process large candidate graphs but may produce substantial fake trajectories, while global optimization enforce stronger trajectory-level consistency at a higher computational cost. We propose ML-QTrack, a learning-guided global optimization framework that combines graph purification with trajectory-level QUBO optimization. On the full-event, full-detector TrackML benchmark, ML-QTrack achieves 98.1% efficiency and reduces the fake rate from 36.71% to 6.87% compared with an HGNN-based method using the same candidate graph, while completing reconstruction in $1.68\text{s}$ per event. These results show that learning-guided hypothesis reduction can make trajectory-level global optimization computationally practical for large-scale full-detector tracking, while substantially improving reconstruction quality.