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Quantum Annealing is a heuristic for solving optimization problems that haveseen a recent surge in usage owing to the success of D-Wave Systems. This paperaims to find a good heuristic for solving the Electric Vehicle ChargerPlacement (EVCP) problem, a problem that stands to be very important given thecosts of setting up an electric vehicle (EV) charger and the expected surge inelectric vehicles across the world. The same problem statement can also begeneralized to the optimal placement of any entity in a grid and can beexplored for further uses. Finally, the authors introduce a novel heuristiccombining Quantum Annealing and Genetic Algorithms to solve the problem. Theproposed hybrid approach entails seeding the genetic algorithms with theresults of quantum annealing. Experimental results show that this methoddecreases the minimum distance from Points of Interest (POI) by $42.89\%$compared to vanilla quantum annealing over the sample EVCP datasets.

Towards an Optimal Hybrid Algorithm for EV Charging Stations Placement using Quantum Annealing and Genetic Algorithms