Quantum-Accelerated Flight Selection: Probing Grover's Algorithm and Quantum Device Efficiency

Current flight search platforms primarily consider four essential factors when planning a trip: departure/arrival dates, as well as the origin and destination locations. However, when additional parameters are added to this search, the problem shifts from a simple to a complex search, as the engine must sift through a massive dataset of flights, including information on airlines, flight routes, fees, and more. To address this challenge and improve flight search efficiency amidst data-intensive and resource-demanding environments, this paper proposes the use of Grover's search algorithm. This algorithm is demonstrated as the optimal solution for searches with increased constraints. The paper highlights the practical application of Grover's search algorithm across three datasets of assorted sizes, as well as various quantum hardware and simulators available in the NISQ era. Furthermore, this paper provides an in-depth understanding of the complexity of quantum circuit design, including the key phases of state encoding and amplitude amplification. The effectiveness of these approaches is evaluated through analysis of execution times and quantum measurement results. The aim is to showcase the potential of quantum computing in revolutionizing real-world search tasks, particularly in the realm of flight selection.