Smart Rebalancing for Bike Sharing Systems using Quantum Approximate Optimization Algorithm

Smart Mobility is the key component of Smart City initiative that are being explored throughout the world. The bike-sharing system (BSS) aims to provide an alternative mode of Smart Mobility transportation system, and it is being widely adopted in urban areas. The use of bikes for short-distance travel helps to reduce traffic congestion, reduce carbon emissions, and decrease the risk of overcrowding. Effective bike sharing system operations requires rebalancing analysis, which corresponds to transferal of bikes across various bike stations to ensure the supply meets expected demand. In this work, we present Quantum Approximate Optimization Algorithm(QAOA), a variational hybrid quantum-classical algorithm that has shown significant computational advantages in solving combinatorial optimization problems such as bike sharing system rebalancing problem (BSS-RBP). Here, we minimize the overall distance travelled by the transport vehicle across various bike station. In this preliminary work, we demonstrate the application of QAOA using the IBM-Qiskit quantum computing simulator for rebalancing analysis across three bike locations.