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

摘要： 摘 要: 随着含噪中等规模量子(Noise Immediate-Scale Quantum, NISQ)时代的到来，量子计算机的硬件限制（如量子比特数量和噪声），给量子电路调度带来了显著挑战。为提高量子算法的实用性，迫切需要优化量子门执行顺序，减少SWAP门插入，从而降低电路深度和运行时间。研究分析了两种量子电路调度优化策略的应用，首次是基于SWAP冲突的深度最优贪心算法和量子锁并行时间的二次电路分层策略(SWAP-Conflict-Based Deep-Optimal Greedy Algorithm and Quantum-Locked Parallel-Time Hierarchical Strategy for Secondary- Circuits, SDGA-QPHSS)，其次为和整体层联合优化SWAP策略(Integral Layer Joint Optimization SWAP Strategy, ILJOSS)的应用。SDGA-QPHSS算法根据SWAP冲突灵活调整量子门的层次结构，针对每一层中的量子门依赖关系和硬件限制，利用量子锁并行来动态选择需要推迟执行的量子门，优化了电路深度和SWAP门的使用；而ILJOSS算法采用跨层优化策略，结合启发式算法和代价函数寻找全局近似最优解，有效优化了量子门冲突和执行顺序。实验结果表明，与传统算法2QAN相比，SDGA-QPHSS算法和ILJOSS算法在硬件映射和复杂的CNOT门约束条件下，减少了86.47%和89.18%的额外门数量，且运行时间分别减少了55.91%和66.18%，有效提升了量子计算效率。

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