2024/10/21 by Tina Radvand, Alireza Talebpour, Radvand, Tina +2
Engineering · #Advanced Battery Technologies Research #Electric Vehicles and Infrastructure #FOS: Mathematics #FOS: Physical sciences #Optimization and Control (math.OC) #Quantum Physics (quant-ph) #Transportation and Mobility Innovations
paper · pdf · doi:10.48550/arxiv.2410.16231
openalex publication_date 2024/10/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Electric vehicles (EVs) play a significant role in enhancing the sustainability of transportation systems. However, their widespread adoption is hindered by inadequate public charging infrastructure, particularly to support long-distance travel. Identifying optimal charging station locations in large transportation networks presents a well-known NP-hard combinatorial optimization problem, as the search space grows exponentially with the number of potential charging station locations. This paper introduces a quantum search-based optimization algorithm designed to enhance the efficiency of solving this NP-hard problem for transportation networks. By leveraging quantum parallelism, amplitude amplification, and quantum phase estimation as a subroutine, the optimal solution is identified with a quadratic improvement in complexity compared to classical exact methods, such as branch and bound. The detailed design and complexity of a resource-efficient quantum circuit are discussed.