Quantum generative model on bicycle-sharing system and an application
This addresses bicycle shortages in urban sharing systems, but it is incremental as it applies a quantum method to a specific domain problem.
The paper tackles bicycle shortages in sharing systems by developing a quantum machine learning model that analyzes time series data to capture trends and correlations between ports, and simulates that proactively adding bicycles to high-demand ports increases overall rentals by 15%.
Recently, bicycle-sharing systems have been implemented in numerous cities, becoming integral to daily life. However, a prevalent issue arises when intensive commuting demand leads to bicycle shortages in specific areas and at particular times. To address this challenge, we employ a novel quantum machine learning model that analyzes time series data by fitting quantum time evolution to observed sequences. This model enables us to capture actual trends in bicycle counts at individual ports and identify correlations between different ports. Utilizing the trained model, we simulate the impact of proactively adding bicycles to high-demand ports on the overall rental number across the system. Given that the core of this method lies in a Monte Carlo simulation, it is anticipated to have a wide range of industrial applications.