NP-hard problems regularly come up in video games, with interesting<br>connections to real-world problems. In the game Minecraft, players place<br>torches on the ground to light up dark areas. Placing them in a way that<br>minimizes the total number of torches to save resources is far from trivial. In<br>this paper, we use Quantum Computing to approach this problem. To this end, we<br>derive a QUBO formulation of the torch placement problem, which we uncover to<br>be very similar to another NP-hard problem. We employ a solution strategy that<br>involves learning Lagrangian weights in an iterative process, adding to the<br>ever growing toolbox of QUBO formulations. Finally, we perform experiments on<br>real quantum hardware using real game data to demonstrate that our approach<br>yields good torch placements.