Quantum Compiling with Reinforcement Learning on a Superconducting Processor
Abstract
To effectively implement quantum algorithms on noisy intermediate-scale quantum (NISQ) processors is a central task in modern quantum technology. NISQ processors feature tens to a few hundreds of noisy qubits with limited coherence times and gate operations with errors, so NISQ algorithms naturally require employing circuits of short lengths via quantum compilation. Here, we develop a reinforcement learning (RL)-based quantum compiler for a superconducting processor and demonstrate its capability of discovering novel and hardware-amenable circuits with short lengths. We show that for the three-qubit quantum Fourier transformation, a compiled circuit using only seven CZ gates with unity circuit fidelity can be achieved. The compiler is also able to find optimal circuits under device topological constraints, with lengths considerably shorter than those by the conventional method. Our study exemplifies the codesign of the software with hardware for efficient quantum compilation, offering valuable insights for the advancement of RL-based compilers.
Cite
@article{arxiv.2406.12195,
title = {Quantum Compiling with Reinforcement Learning on a Superconducting Processor},
author = {Z. T. Wang and Qiuhao Chen and Yuxuan Du and Z. H. Yang and Xiaoxia Cai and Kaixuan Huang and Jingning Zhang and Kai Xu and Jun Du and Yinan Li and Yuling Jiao and Xingyao Wu and Wu Liu and Xiliang Lu and Huikai Xu and Yirong Jin and Ruixia Wang and Haifeng Yu and S. P. Zhao},
journal= {arXiv preprint arXiv:2406.12195},
year = {2024}
}