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

Abstract

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.

Accepted at 18th ACM International Symposium on Nanoscale Architectures