AI 中文总结
该研究提出统一不确定性量化框架，在同一统计管道下比较NISQ变分量子算法和基于QSVT的格林函数重建两类工作负载，结合多种分析方法，能识别相关特性，为不同工作负载提供通用基准来衡量后端可靠性。
AI 中文摘要
我们提出了一个用于噪声量子后端的应用级基准测试和表征的不确定性量化（UQ）框架。该框架在一个统计管道下比较两类工作负载：噪声中等规模量子（NISQ）变分量子算法（VQA）和基于量子奇异值变换（QSVT）的格林函数重建。对于VQA分支，评估了十个基准系列。对于QSVT分支，从块编码实时传播器重建轨道分辨格林函数和光谱峰值。工作流程结合了贝叶斯优化等多种分析。该框架能识别稳健参数区域等，为变分和非变分工作负载提供通用基准，衡量每个后端达到有用任务级行为的可靠程度。
英文摘要
We present an uncertainty quantification (UQ) framework for application level benchmarking and characterization of noisy quantum backends. The framework compares two workload classes under one statistical pipeline: noisy intermediate scale quantum (NISQ) variational quantum algorithms (VQAs) and Quantum Singular Value Transformation (QSVT) based Green's function reconstruction. For the VQA branch, we evaluate ten benchmark families spanning chemistry, optimization, simulation, compiling, linear solving, partial differential equations, metrology, error correction, tomography, and channel fidelity estimation. For the QSVT branch, we reconstruct orbital resolved Green's functions and spectral peaks from a block encoded real time propagator. The workflow combines Bayesian optimization, posterior distribution refinement, sensitivity analysis, robust parameter density estimation, backend ranking, noise correlation, and resource estimation analysis. Instead of reporting only one best parameter vector, the framework identifies robust parameter regions, residual gaps to ideal behavior, backend specific failure modes, and calibration sensitive uncertainty. The result is a common benchmark for variational and non-variational workloads that measures how reliably each backend reaches useful task level behavior.
