TY - GEN
T1 - T-count Reduction Method Based on Proximal Policy Optimization
AU - Xiong, Keyu
AU - Shang, Tao
AU - Zhang, Chenyi
AU - Liu, Yuchen
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - In fault-tolerant quantum computing systems, T gates consume more fault-tolerant resources. In this paper, we propose a T-count reduction method based on the Proximal Policy Optimization (PPO) algorithm, minimizing the number of T gates in quantum computations. Initially, within the framework of ZX-calculus graphical language, quantum circuits are transformed into ZX-diagrams. Subsequently, the PPO algorithm is employed to learn a policy that predicts optimal transformation trajectories. To effectively leverage the topological structure of ZX-diagrams, we employ graph neural networks (GNNs) to encode the policy trained via PPO algorithm, while identifying possible transformations through the local structural properties of individual nodes or edges. The proposed method achieves an average 10.17% reduction in T-count under optimal conditions, demonstrating its capability in reducing the number of T gates.
AB - In fault-tolerant quantum computing systems, T gates consume more fault-tolerant resources. In this paper, we propose a T-count reduction method based on the Proximal Policy Optimization (PPO) algorithm, minimizing the number of T gates in quantum computations. Initially, within the framework of ZX-calculus graphical language, quantum circuits are transformed into ZX-diagrams. Subsequently, the PPO algorithm is employed to learn a policy that predicts optimal transformation trajectories. To effectively leverage the topological structure of ZX-diagrams, we employ graph neural networks (GNNs) to encode the policy trained via PPO algorithm, while identifying possible transformations through the local structural properties of individual nodes or edges. The proposed method achieves an average 10.17% reduction in T-count under optimal conditions, demonstrating its capability in reducing the number of T gates.
KW - Proximal policy optimization
KW - Quantum circuit optimization
KW - Reinforcement learning
KW - T-count reduction
KW - ZX-calculus
UR - https://www.scopus.com/pages/publications/105029041709
U2 - 10.1007/978-981-95-4791-3_2
DO - 10.1007/978-981-95-4791-3_2
M3 - 会议稿件
AN - SCOPUS:105029041709
SN - 9789819547906
T3 - Communications in Computer and Information Science
SP - 17
EP - 25
BT - Quantum Computation - 4th CCF Quantum Computation Conference, CQCC 2025, Proceedings
A2 - Li, Xiaoyu
A2 - Wu, Junjie
A2 - Zhang, Jialin
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th CCF Quantum Computation Conference, CQCC 2025
Y2 - 21 July 2025 through 23 July 2025
ER -