AI 中文总结
本研究在受限执行预算下，结合多种技术在IBM Quantum处理器上开展实验，探究超导量子硬件的误差缓解与有限shots采样，为相关研究提供可复现的硬件感知实验证据。
AI 中文摘要
我们在受限执行预算下对超导量子硬件上的误差缓解与有限 shots 采样开展实验研究。该研究结合了感知校准的量子比特选择、电路深度缩放、零噪声外推、动力学去耦、读出误差缓解以及在 IBM Quantum 处理器上的重复 shots 估计。实验分为理想模拟、噪声模型模拟和物理设备执行三类，以分离采样不确定性与设备诱导误差。我们探究了缓解性能随电路深度、有效噪声尺度、量子比特连接性和测量预算的变化情况，并通过期望值误差、均方误差、统计不确定性和缓解增益量化精度。采用固定硬件执行预算评估 shots 分配策略与重复测量，不依赖无限采样。所得分析提供了硬件感知的表征，阐明了何时缓解能改进期望值估计、何时有限 shots 波动会抵消额外缓解开销的益处。实现采用了当代 Qiskit 与 IBM Quantum Runtime 工作流，旨在为当前量子处理器上的误差缓解研究提供可复现的实验证据。
英文摘要
We experimentally study error mitigation and finite-shot sampling on superconducting quantum hardware under a constrained execution budget. The study combines calibration-aware qubit selection, circuit-depth scaling, zero-noise extrapolation, dynamical decoupling, readout-error mitigation, and repeated-shot estimation on an IBM Quantum processor. Experiments are organized across ideal simulation, noise-model simulation, and physical-device execution to separate sampling uncertainty from device-induced error. We investigate how mitigation performance changes with circuit depth, effective noise scale, qubit connectivity, and measurement budget, and quantify accuracy using expectation-value error, mean-squared error, statistical uncertainty, and mitigation gain. A fixed hardware-execution budget is used to evaluate shot allocation strategies and repeated measurements without relying on unlimited sampling. The resulting analysis provides a hardware-aware characterization of when mitigation improves expectation-value estimation and when finite-shot fluctuations offset the benefit of additional mitigation overhead. The implementation uses contemporary Qiskit and IBM Quantum Runtime workflows and is designed to provide reproducible experimental evidence for error-mitigation studies on current quantum processors.
Comments16 pages, 9 figures, 6 tables. Experiments performed on IBM Quantum hardware using Qiskit 2.5.0 and Qiskit Runtime 0.48.0. Includes hardware calibration analysis, zero-noise extrapolation, dynamical decoupling, readout mitigation, adaptive shot allocation, repeated hardware experiments, and scaling studies
