Quantum generative model on bicycle-sharing system and an application

2025-10-06

AI Summary

To address the pervasive vehicle shortage in bike-sharing systems during peak hours and at hotspot locations, this paper proposes a quantum machine learning–based generative modeling approach. The method jointly models multivariate time-series data of bike counts across stations by approximating a quantum time-evolution operator, thereby capturing dynamic inter-station correlations and long-term temporal trends. It further integrates Monte Carlo simulation to enable system-level demand forecasting and dispatch scenario inference. Compared to classical models, our approach demonstrates superior capability in uncovering latent spatial–temporal dependencies and jointly predicting cross-station demand. Experimental results show that a proactive rebalancing strategy derived from this model increases the system-wide rental rate by 12.7%, validating the feasibility and practical value of quantum generative models for urban mobility forecasting.

Technology Category

• Machine Learning: Quantum Machine Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Addressing bicycle shortages during peak commuting demand periods
• Analyzing time series data using quantum machine learning models
• Simulating impacts of proactive bicycle redistribution on system efficiency

Innovation

• Quantum machine learning models time series data
• Fits quantum evolution to bicycle count sequences
• Monte Carlo simulation predicts system-wide rental impact