A hybrid learning agent approach for solving flight trajectory optimization

Kurzfassung

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.