SWAP Optimization for Qubit Mapping Based on the Centric-Shortest Quantum Gate Set in NISQ Devices

In the Quantum computing era of Noisy Intermediate-Scale Quantum (NISQ) devices, conventional qubit mapping strategies typically rely on specific heuristic rules to solve the mapping problem, overlooking the impact of other factors on the mapping, which leads to increased overhead from extra SWAP gates. To address this issue, we propose a SWAP optimization strategy based on the Centric-Shortest Quantum Gate Set (C-SQGS) and applies it to qubit mapping. In this approach, the centric qubit is determined by analyzing the maximum flexibility qubit set and the physical distances between the associated CNOT gates, leading to the identification of the Centric-Shortest Quantum Gate Set. To overcome the limitations of traditional cost functions that consider only single factors, a multi-factor cost function is introduced to evaluate the overall overhead of candidate SWAP operations and determine pending SWAP gate Set. Based on qubit flexibility analysis, executable SWAP gate is identified and inserted into the circuit. Experimental results demonstrate that the C-SQGS strategy effectively reduces both SWAP gate and two-qubit gate overhead. Specifically, it achieves an average SWAP gate reduction of 36.9% and 47.7%, and a two-qubit gate reduction of 13.8% and 13.5% on the t|ket⟩ and Qiskit compilers, respectively. These results highlight the potential of the C-SQGS strategy in enhancing the efficiency of qubit mapping for NISQ devices.

@article{Liu2025,
  title     = {SWAP Optimization for Qubit Mapping Based on the Centric-Shortest Quantum Gate Set in NISQ Devices},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Shujuan Liu and Hui Li and Yingsong Ji and Jiepeng Wang},
  doi       = {10.14569/IJACSA.2025.0161284},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161284}
}