A Quantum Optimization Algorithm for Optimal Electric Vehicle Charging Station Placement for Intercity Trips

TL;DR

This paper presents a quantum algorithm that significantly improves the efficiency of locating optimal electric vehicle charging stations in large transportation networks, addressing a complex NP-hard problem with potential for real-world impact.

Contribution

It introduces a novel quantum search-based optimization algorithm that leverages quantum parallelism and amplitude amplification to solve the EV charging station placement problem more efficiently than classical methods.

Findings

Quadratic reduction in computational complexity compared to classical algorithms

Design of a resource-efficient quantum circuit for the optimization task

Potential for practical implementation in large transportation networks

Abstract

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.