Quantum Multi-Strategy Fusion Mapping Optimization: An Algorithm for Quantum Circuit
Zhenguo Yuan, Jiepeng Wang, Hui Li, Yutong Chen · IEEE Access · 2025
In response to the current challenges in quantum circuit mapping algorithms, such as low mapping quality, insufficient diversity, and the difficulty in balancing mapping quality and diversity, a Quantum Multi-Strategy Fusion Mapping Optimization Algorithm (QMS-FMO) is proposed in this paper. This algorithm enhances population diversity by integrating the neighboring gate strategy of Genetic Algorithm-based Quantum Circuit Mapping (GAQCM), the quantum state characteristics of QPSO, and the parameter adjustment strategy of GSQPSO. The fitness function, which comprehensively considers SWAP gates, CNOT gate costs, particle position information, and quantum behavioral factors, is designed with an adaptive adjustment mechanism to accurately evaluate mapping solutions. During solution updating, individuals are selected using the GAQCM selection strategy, and position update formulas from QPSO and GSQPSO are combined with crossover and mutation operations based on qubit coupling strength, leveraging quantum characteristics to avoid local optima. Experimental results demonstrate that, compared with the t|ket> and Qiskit compilers, QMS-FMO reduces the number of inserted SWAP gates by 46.1% compared to t|ket> and 65.2% compared to Qiskit for specific qubit scales, significantly improving the quality and efficiency of quantum circuit mapping.