Quantum Compiling with Reinforcement Learning on a Superconducting Processor

• Main Authors: Wang, Z. T, Chen, Qiuhao, Du, Yuxuan, Yang, Z. H, Cai, Xiaoxia, Huang, Kaixuan, Zhang, Jingning, Xu, Kai, Du, Jun, Li, Yinan, Jiao, Yuling, Wu, Xingyao, Liu, Wu, Lu, Xiliang, Xu, Huikai, Jin, Yirong, Wang, Ruixia, Yu, Haifeng, Zhao, S. P
• Format: Journal Article
• Language: English
• Published: 17-06-2024
• Subjects: Computer Science - Learning; Physics - Quantum Physics
• DOI: 10.48550/arxiv.2406.12195

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.