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Reinforcement Learning for Quantum Circuit Design: Using Matrix Representations
Zhiyuan Wang, Chunlin Feng, Christopher Poon, Lijian Huang, Xingjian Zhao, Yao Ma, Tianfan Fu, Xiao-yang Liu·January 27, 2025·DOI: 10.48550/arXiv.2501.16509
PhysicsComputer Science
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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.