Quantum generative model on bicycle-sharing system and an application

Abstract

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.

8 pages, 11 figures

References in corpus (5)

• Quantum Convolutional Neural Networks
• Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach
• Experimental Quantum Generative Adversarial Networks for Image Generation
• Modeling Bike Share Station Activity: Effects of Nearby Businesses and Jobs on Trips to and from Stations
• Modeling bike counts in a bike-sharing system considering the effect of weather conditions