We study the flight gate assignment problem on IonQ's Aria trapped ion quantum computer using the variational quantum eigensolver. Utilizing the conditional value at risk as an aggregation function, we demonstrate that current trapped ion quantum hardware is able to obtain good solutions for this combinatorial optimization problem with high probability. In particular, we run the full variational quantum eigensolver for small instances and we perform inference runs for larger systems, demonstrating that current and near-future quantum hardware is suitable for addressing combinatorial optimization problems.
