Data for Quantum Circuit Compression Based on Machine Learning (IBM Eagle gate set)

The circuits in this repository correspond to the benchmark set introduced by Nam et al. [1], optimized for the IBM-Eagle gate set using QMill's algorithm. Our method achieves superior results compared to the previous state-of-the-art approach, Quarl [2].

The original circuits are available at the following link. They should be transpiled to the IBM-Eagle gate set ({CX, SX, X, Rz}) using Qiskit before optimization.

 

https://github.com/njross/optimizer

 

[1] Y. Nam, N.J. Ross, Y. Su, A.M. Childs, and D. Maslov. Automated optimization of large quantum circuits with continuous parameters. October 2017. Available from https://arxiv.org/abs/1710.07345.

 

[2] Z. Li, J. Peng, Y. Mei, S. Lin, Y. Wu, O. Padon, and Z. Jia. 2024. Quarl: A Learning-Based Quantum Circuit Optimizer. Proc. ACM Program. Lang. 8, OOPSLA1, Article 114 (April 2024), 28 pages. https://doi.org/10.1145/3649831