  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
generalized 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 algorithms with the
results of quantum annealing. Experimental results show that this method
decreases the minimum distance from Points of Interest (POI) by $42.89\%$
compared to vanilla quantum annealing over the sample EVCP datasets.
