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Reinforcement learning for the optimization of quantum circuits uses an agentwhose goal is to maximize the value of a reward function that decides what iscorrect and what is wrong during the exploration of the search space. It is anopen problem how to formulate reward functions that lead to fast and efficientlearning. We propose an exponential reward function which is sensitive tostructural properties of the circuit. We benchmark our function on circuitswith known optimal depths, and conclude that our function is reducing thelearning time and improves the optimization. Our results are a next steptowards fast, large scale optimization of quantum circuits.

Towards Faster Reinforcement Learning of Quantum Circuit Optimization: Exponential Reward Functions