Hybrid classical–quantum ensemble learning for real-time flight delay prediction at Tribhuvan International Airport

Abstract

This study investigates ensemble learning using classical and quantum-inspired models to predict flight delays at Tribhuvan International Airport (TIA), Nepal. It combines traditional machine learning algorithms with quantum-based approaches, quantum boosting (QBoost) and the hybrid QBoostPlus, leveraging quantum properties for faster computation. The dataset includes flight records from 2020 to 2024 and Meteorological Aerodrome Reports (METAR), analyzed across four sea- sons to capture delay patterns in domestic and international flights. A combined seasonal dataset assesses model generalization. Six models; VotingClassifier, adaptive boosting (AdaBoost), xtreme gradient boosting (XGBoost), categorical boosting (CatBoost), QBoost, and QBoostPlus are evaluated based on accuracy, precision, recall, F1 score, area under the curve(AUC), and execution time. CatBoost achieved high accuracy (up to 0.97) but slower execution (up to 10,570.63 ms). QBoostPlus provides competitive AUC scores (0.83–0.95) with faster execution, improving speed by up to 99.94% and generating predictions in as little as 6.46 ms. Although quantum-inspired models have slightly lower accuracy, their computational efficiency and stability show strong potential for real-time flight delay prediction. This is the first study applying quantum-inspired ensemble learning to Nepalese aviation data, showing promise for regional airports with limited infrastructure.