Application of quantum computing in choice-based network revenue management

This paper explores the potential of quantum computing for solving the choice-based deterministic linear program (CDLP) for network revenue management. The CDLP is a model that incorporates customer choice between parallel itineraries serving the same origin–destination market and has been shown to offer significant revenue benefits compared to traditional methods. However, the CDLP is computationally intractable for realistic network sizes due to the exponential growth of the offer set space. We propose a novel quantum algorithm that uses quantum column generation and quantum search to achieve a quadratic speed-up over the classical counterparts. We demonstrate the feasibility and performance of our quantum algorithm on a toy network example and discuss the implications and challenges for future applications. Our work stands as the first of its kind to apply quantum computing to the domain of airline revenue management.