Running quantum programs is fraught with challenges on on today's noisy intermediate scale quantum (NISQ) devices. Many of these challenges originate from the error characteristics that stem from rapid decoherence and noise during measurement, qubit connections, crosstalk, the qubits themselves, and transformations of qubit state via gates. Not only are qubits not "created equal", but their noise level also changes over time. IBM is said to calibrate their quantum systems once per day and reports noise levels (errors) at the time of such calibration. This information is subsequently used to map circuits to higher quality qubits and connections up to the next calibration point. This work provides evidence that there is room for improvement over this daily calibration cycle. It contributes a technique to measure noise levels (errors) related to qubits immediately before executing one or more sensitive circuits and shows that just-in-time noise measurements benefit late physical qubit mappings. With this just-in-time recalibrated transpilation, the fidelity of results is improved over IBM's default mappings, which only uses their daily calibrations. The framework assess two major sources of noise, namely readout errors (measurement errors) and two-qubit gate/connection errors. Experiments indicate that the accuracy of circuit results improves by 3-304% on average and up to 400% with on-the-fly circuit mappings based on error measurements just prior to application execution.

中文翻译：

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即时量子电路转译降低噪声

在当今嘈杂的中型量子 (NISQ) 设备上运行量子程序充满挑战。其中许多挑战源于测量过程中的快速退相干和噪声、量子位连接、串扰、量子位本身以及通过门进行的量子位状态转换所产生的误差特征。不仅量子位不是“生来平等”的，而且它们的噪声水平也会随着时间而变化。据说 IBM 每天校准一次他们的量子系统，并在校准时报告噪声水平（错误）。该信息随后用于将电路映射到更高质量的量子位和连接到下一个校准点。这项工作提供的证据表明，这一日常校准周期仍有改进的余地。它提供了一种在执行一个或多个敏感电路之前立即测量与量子位相关的噪声水平（错误）的技术，并表明即时噪声测量有益于后期物理量子位映射。通过这种及时重新校准的转译，结果的保真度比 IBM 的默认映射有所提高，后者仅使用他们的日常校准。该框架评估了两个主要的噪声源，即读出错误（测量错误）和双量子位门/连接错误。实验表明，电路结果的准确度平均提高了 3-304%，使用基于应用程序执行前的误差测量的实时电路映射可提高 400%。该框架评估了两个主要的噪声源，即读出错误（测量错误）和双量子位门/连接错误。实验表明，电路结果的准确度平均提高了 3-304%，使用基于应用程序执行前的误差测量的动态电路映射提高了 400%。该框架评估了两个主要的噪声源，即读出错误（测量错误）和双量子位门/连接错误。实验表明，电路结果的准确度平均提高了 3-304%，使用基于应用程序执行前的误差测量的实时电路映射可提高 400%。