关键词: 量子电路调度, SWAP冲突, 深度最优贪心算法, 量子锁并行, 整体层联合优化

Abstract: Abstract: With the advent of the Noise Immediate-Scale Quantum (NISQ) era, the hardware limitations of quantum computers (e.g., the number of quantum bits and noise), pose significant challenges to quantum circuit scheduling. To improve the practicality of quantum algorithms, there is an urgent need to optimize the quantum gate execution order and reduce SWAP gate insertions, thereby reducing circuit depth and runtime. In this paper, the application of two quantum circuit scheduling optimization strategies is analyzed, the first is the SWAP-Conflict-Based Deep-Optimal Greedy Algorithm and Quantum-Locked Parallel-Time Hierarchical Strategy for Secondary-Circuits (SDGA-QPHSS), followed by the Integral Layer Joint Optimization SWAP Strategy (ILJOSS).The study analyzes the SWAP-Conflict-Based Deep-Optimal Greedy Algorithm and Quantum-Locked Parallel-Time Hierarchical Strategy for Secondary-Circuits (SDGA-QPHSS) and Integral Layer Joint Optimization SWAP Strategy (ILJOSS). The SDGA-QPHSS algorithm flexibly adjusts the hierarchy of quantum gates according to the SWAP conflicts. The SDGA-QPHSS algorithm flexibly adjusts the hierarchical structure of quantum gates for the quantum gate dependencies and hardware constraints in each layer, uses quantum lock parallelism to dynamically select the quantum gates that need to be delayed for execution, which optimizes the circuit depth and the use of SWAP gates; whereas the ILJOSS algorithm adopts a cross-layer optimization strategy that combines heuristic algorithms and cost functions to find a globally near-optimal solution, which efficiently optimizes the quantum gate conflicts and execution order. The experimental results show that compared with the traditional algorithm 2QAN, the SDGA-QPHSS algorithm and the ILJOSS algorithm reduce the number of extra gates by 86.47% and 89.18% with hardware mapping and complex CNOT gate constraints, and the running time is reduced by 55.91% and 66.18%, respectively, which effectively improves the efficiency of quantum computation.

Key words: Keywords: quantum circuit scheduling, swap-conflict, deep-optimal greedy algorithm, quantum lock parallelism, integral layer joint optimization