Dresden 2026 – wissenschaftliches Programm
Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe
QI: Fachverband Quanteninformation
QI 5: Quantum Computing and Algorithms II
QI 5.10: Vortrag
Dienstag, 10. März 2026, 12:15–12:30, BEY/0137
A hybrid learning agent approach for solving the flight trajectory optimization — •Marcel Schindler1, Rouven Kanitz2, and Sabine Wölk1 — 1Institute of quantum technologies, DLR, Ulm, Germany — 2Institute of air transport, DLR, Hamburg, Germany
It is believed that combinatorial optimization is to be among the first applications where quantum computers can demonstrate a practical advantage over classical systems. One such problem is the flight trajectory optimization, which aims to find the optimal path between two airports for an aircraft under given conditions. Due to non-local constraints of the flight path, the problem is part of complexity class NP-hard and becomes difficult to solve for classical algorithms. To address this, we employ a reinforcement learning algorithm in which the learning process of an agent is sped up by using a quantum communication channel.
Keywords: Quantum Machine Learning; Grover Adaptive Search; Reinforcement learning; Trajectory optimization