Applying the Quantum Approximate Optimization Algorithm to the Tail Assignment Problem: part 1

Noisy intermediate-scale quantum (NISQ) devices, composed of a few tenths of qubits are now starting to become available. They have been shown to be able to perform some tasks that are hard to replicate on a supercomputer. However, these tasks have not been linked yet with the solution of useful problems. A relevant question is therefore whether NISQs devices can also solve real-world problems. In this work, we study numerically the solution of an airline optimization problem, namely the Tail-Assignment problem (TAS), on near-term quantum processors composed of up to 25 qubits, by using the quantum approximate optimization algorithm (QAOA).