Quantum Annealing is a heuristic for solving optimization problems that have seen a recent surge in usage owing to the success of D-Wave Systems. This paper aims to find a good heuristic for solving the Electric Vehicle Charger Placement (EVCP) problem, a problem that stands to be very important given the costs of setting up an electric vehicle (EV) charger and the expected surge in electric vehicles across the world. The same problem statement can also be generalised to the optimal placement of any entity in a grid and can be explored for further uses. Finally, the authors introduce a novel heuristic combining Quantum Annealing and Genetic Algorithms to solve the problem. The proposed hybrid approach entails seeding the genetic algorithm with the results of a quantum annealer. Our experiments show this method decreases the minimum distance from POIs by 42.89% compared to vanilla quantum annealing over our sample EVCP datasets.

翻译：Qantum Annaaling是解决优化问题的一种杂技,由于D-Wave Systems的成功,最近由于D-Wave Systems的成功,使用量激增。本文旨在找到解决电动车辆充电装置(EVCP)问题的灵丹妙药。鉴于在世界各地安装电动车辆充电器的成本和电动车辆的预期激增,这个问题非常重要。同样的问题说明也可以被概括到将任何实体放在电网中的最佳位置上,并可以探索进一步使用。最后,作者提出了一种新的将Qantum Annaaling和遗传阿尔戈蒂姆相结合的超常药,以解决问题。拟议的混合方法意味着以量子射精器的结果检测遗传算法。我们的实验显示,与POIs的最小距离将减少42.89%,而与Vanilla 量子射去我们样本EVECCP数据集的最小距离将减少42.89%。