Reinforcement Learning for Quantum Circuit Design: Using Matrix Representations
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909468612100096 |
|---|---|
| author | Wang, Zhiyuan Feng, Chunlin Poon, Christopher Huang, Lijian Zhao, Xingjian Ma, Yao Fu, Tianfan Liu, Xiao-Yang |
| author_facet | Wang, Zhiyuan Feng, Chunlin Poon, Christopher Huang, Lijian Zhao, Xingjian Ma, Yao Fu, Tianfan Liu, Xiao-Yang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16509 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning for Quantum Circuit Design: Using Matrix Representations Wang, Zhiyuan Feng, Chunlin Poon, Christopher Huang, Lijian Zhao, Xingjian Ma, Yao Fu, Tianfan Liu, Xiao-Yang Quantum Physics Artificial Intelligence 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. |
| title | Reinforcement Learning for Quantum Circuit Design: Using Matrix Representations |
| topic | Quantum Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2501.16509 |