NP-hard problems regularly come up in video games, with interestingconnections to real-world problems. In the game Minecraft, players placetorches on the ground to light up dark areas. Placing them in a way thatminimizes the total number of torches to save resources is far from trivial. Inthis paper, we use Quantum Computing to approach this problem. To this end, wederive a QUBO formulation of the torch placement problem, which we uncover tobe very similar to another NP-hard problem. We employ a solution strategy thatinvolves learning Lagrangian weights in an iterative process, adding to theever growing toolbox of QUBO formulations. Finally, we perform experiments onreal quantum hardware using real game data to demonstrate that our approachyields good torch placements.