Reinforcement Learning for Quantum Circuit Design: Using Matrix Representations
Quantum Physics
2025-01-29 v1 Artificial Intelligence
Abstract
Quantum computing promises advantages over classical computing. The manufacturing of quantum hardware is in the infancy stage, called the Noisy Intermediate-Scale Quantum (NISQ) era. A major challenge is automated quantum circuit design that map a quantum circuit to gates in a universal gate set. In this paper, we present a generic MDP modeling and employ Q-learning and DQN algorithms for quantum circuit design. By leveraging the power of deep reinforcement learning, we aim to provide an automatic and scalable approach over traditional hand-crafted heuristic methods.
Cite
@article{arxiv.2501.16509,
title = {Reinforcement Learning for Quantum Circuit Design: Using Matrix Representations},
author = {Zhiyuan Wang and Chunlin Feng and Christopher Poon and Lijian Huang and Xingjian Zhao and Yao Ma and Tianfan Fu and Xiao-Yang Liu},
journal= {arXiv preprint arXiv:2501.16509},
year = {2025}
}