标题： 重新审视量子计算中的噪声自适应编译：它有多大影响？ 显示英文标题

标题： Revisiting Noise-adaptive Transpilation in Quantum Computing: How Much Impact Does it Have?

摘要： 量子电路的编译，尤其是考虑噪声的优化，被广泛认为对于在超导量子计算机上最大化电路性能是至关重要的。普遍的观点是，每个电路都应使用最新的噪声校准数据进行编译以优化保真度。在本工作中，我们重新审视频繁适应噪声的编译的必要性，在五个IBM 127量子位量子计算机和16种不同的量子算法上进行了深入的实证研究。我们的研究结果揭示了新颖而有趣的见解：(1) 考虑噪声的编译会导致工作负载集中在一小部分量子位上，这会增加输出错误的变异性；(2) 使用随机映射可以缓解这种影响，同时保持相当的平均保真度；(3) 使用校准数据一次编译的电路可以在多个校准周期和时间段内可靠地重复使用，而不会显著降低保真度。这些结果表明，与日常的、每个电路的噪声感知编译相关的经典开销可能并不合理。我们提出了轻量级的替代方案，以减少这种开销而不牺牲保真度——为更高效和可扩展的量子工作流程提供了一条路径。 显示英文摘要

摘要： Transpilation, particularly noise-aware optimization, is widely regarded as essential for maximizing the performance of quantum circuits on superconducting quantum computers. The common wisdom is that each circuit should be transpiled using up-to-date noise calibration data to optimize fidelity. In this work, we revisit the necessity of frequent noise-adaptive transpilation, conducting an in-depth empirical study across five IBM 127-qubit quantum computers and 16 diverse quantum algorithms. Our findings reveal novel and interesting insights: (1) noise-aware transpilation leads to a heavy concentration of workloads on a small subset of qubits, which increases output error variability; (2) using random mapping can mitigate this effect while maintaining comparable average fidelity; and (3) circuits compiled once with calibration data can be reliably reused across multiple calibration cycles and time periods without significant loss in fidelity. These results suggest that the classical overhead associated with daily, per-circuit noise-aware transpilation may not be justified. We propose lightweight alternatives that reduce this overhead without sacrificing fidelity -- offering a path to more efficient and scalable quantum workflows.