Abstract: With the rapid development of quantum computing technology,the running time of quantum circuits and the cost of additional gate insertions have become the main challenges in achieving efficient quantum circuit scheduling.To address these,this paper proposes a multi-strategy fusion crayfish optimization algorithm(MPF-COA).By customizing the initial population and combining the two strategies of entropy-based adaptive mechanism and pheromone transmission mechanism,the efficiency and accuracy of the algorithm are optimized,and the optimization efficiency of quantum circuit scheduling under complex constraints is significantly improved.The optimization of the initial population employs dependency-based SWAP insertion strategy(DBSI),ensuring a higher-quality starting solution.On this basis,the entropy-based adaptive temperature adjustment mechanism avoids getting trapped in local optimal solutions.The pheromone transmission mechanism effectively improves the search efficiency for the global optimal solution by guiding the search direction.The performance evaluation is conducted using the 2QAN quantum computing framework on a benchmark test set with qubit scales ranging from 4 to 22.The results show that compared with 2QAN,MPF-COA reduces the average number of SWAP gates by approximately 3.2% in t|ket〉 and by approximately 10.68% in Qiskit,and reduces the number of CNOT gates by approximately 4.69% and 11.89%,respectively.This study demonstrates the potential for the deep integration of bionic algorithms and quantum circuit scheduling,providing a sustainable research foundation for future scheduling and optimization of larger-scale quantum circuits.

Key words: Quantum circuit scheduling, Crayfish optimization algorithm, Multi-strategy fusion, Entropy adaptive, Qubit dependence