Quantum Computing for Airline Planning and Operations

Licentiate thesis, 2023

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.