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.
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About the speaker: Dr. Talebpour is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign. He has more than 12 years of experience in teaching, research, and consulting in vehicle automation, connected transportation system, traffic analysis, and traffic flow theory. He was leading Texas A&M’s team in SAE/GM AutoDrive Challenge (before joining UIUC), a four-year competition to develop a fully (level 4) autonomous vehicle among eight Universities. He is currently leading an FHWA-funded project focusing on trajectory data collection from CAV operations in highway and arterial environments, “Third Generation Simulation Data (TGSIM)”. He has been working on developing algorithms for vehicle safety and efficiency in a connected and automated driving environment and has developed simulation tools to simultaneously simulate wireless communications and drivers and automated vehicles.