Reinforcement Learning for Quantum Circuit Design: Using Matrix Representations

• URL: http://arxiv.org/abs/2501.16509v1
• Date: Mon, 27 Jan 2025 21:17:58 GMT
• Title: Reinforcement Learning for Quantum Circuit Design: Using Matrix Representations
• Authors: Zhiyuan Wang, Chunlin Feng, Christopher Poon, Lijian Huang, Xingjian Zhao, Yao Ma, Tianfan Fu, Xiao-Yang Liu,
• Abstract summary: 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 methods.
• Score: 23.341157852018377
• License:
• 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.

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