Simulating the flight gate assignment problem on a trapped ion quantum computer

TL;DR

This paper explores using a trapped ion quantum computer to solve the flight gate assignment problem, showing that current hardware can effectively address combinatorial optimization tasks.

Contribution

It demonstrates the application of the variational quantum eigensolver on IonQ's hardware for solving a real-world combinatorial optimization problem.

Findings

Current hardware can find good solutions with high probability.

Full VQE runs are feasible for small instances.

Inference runs are effective for larger instances.

Abstract

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.