  Reinforcement learning for the optimization of quantum circuits uses an agent
whose goal is to maximize the value of a reward function that decides what is
correct and what is wrong during the exploration of the search space. It is an
open problem how to formulate reward functions that lead to fast and efficient
learning. We propose an exponential reward function which is sensitive to
structural properties of the circuit. We benchmark our function on circuits
with known optimal depths, and conclude that our function is reducing the
learning time and improves the optimization. Our results are a next step
towards fast, large scale optimization of quantum circuits.
