Quantum Computing for Airline Planning and Operations

Abstract: Classical algorithms and mathematical optimization techniques have been used extensively by airlines to optimize their profit and ensure that regulations are followed. In this thesis, we explore which role quantum algorithms can have for airlines. Specifically, we have considered the two quantum optimization algorithms; the Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing (QA). We present a heuristic that integrates these quantum algorithms into the existing classical algorithm, which is currently employed to solve airline planning problems in a state-of-the-art commercial solver. We perform numerical simulations of QAOA circuits and find that linear and quadratic algorithm depth in the input size can be required to obtain a one-shot success probability of 0.5. Unfortunately, we are unable to find performance guarantees. Finally, we perform experiments with D-wave’s newly released QA machine and find that it outperforms 2000Q for most instances.