Optimizing Commercial Flights using QAOA
By Armaan
This project aims to optimize a user’s flight itinerary by combining fare forecasting with flight route optimization. This project examines two independent methods to reduce air travel costs. The first leverages a classical approach: machine learning models forecast fares, and graph search methods such as A* build multistop itineraries under user preferences. The second uses quantum computing for both forecasting and route selection, including a Quantum Approximate Optimization Algorithm to formulate an itinerary. The study measures how each approach performs in terms of accuracy, runtime, and usability, and reports where quantum methods provide an advantage and where classical computing does the job. The goal is to provide an accurate comparison of both approaches when applied to fare-optimized itinerary building.