发表机构
National University of Computer and Emerging Sciences (FAST-NUCES); City University of Science and Information Technology; University of Engineering and Technology（国立计算机与新兴科学大学（FAST-NUCES）; 城市科学与信息技术大学; 工程技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于证据的量子资源估计框架，发现标准估计器在实测错误率下无法表示容错工作负载，并量化了硬件参数与成本模型带来的巨大不确定性。
AI 中文摘要
运行容错算法所需的量子资源估计几乎总是以单一数字报告，尽管这些估计依赖于不确定的硬件参数和相互不一致的成本模型。我们提出了一个开放、工具无关的框架，该框架将基于证据的容错硬件参数先验传播通过五个表面码成本模型，加入相关输入敏感性分析，并通过预注册的三层协议评估所得区间。将该框架应用于超导表面码架构上的RSA-2048、ECC-256、AES-256和Hubbard模型模拟，产生了三个相互关联的发现，共同削弱了单一数字惯例。传播硬件实际演示的错误率（五个大型阵列设备的中位数约为4×10^-3，而非常规的10^-3）使90%物理量子比特区间扩大约四十倍，并将其位数中位数提升至常规点估计的约4.6倍。针对相同输入，五个独立结构的成本模型（包括两个第三方估计器：Azure Quantum Resource Estimator和Qualtran，它们彼此一致在约百分之十以内）却存在稳定的两倍差异，这是一种任何单一工具都无法揭示的结构性不确定性。最具有后果性的是，两个第三方估计器在80%至100%的基于证据的参数空间中超出其可表示范围，且AES-256门数直接溢出其中一个工具的计数器，因此这些工作负载根本无法在实测错误率下被表示。乐观的惯例掩盖了所有这些效应。我们使用的不确定性机制是标准的。贡献在于其基于证据的应用、预注册的评估，以及标准工具恰恰在硬件实际运行的条件下失效的发现。
英文摘要
Estimates of the quantum resources needed to run fault-tolerant algorithms are almost always reported as single numbers, even though they rest on uncertain hardware parameters and on cost models that disagree. We present an open, tool-agnostic framework that propagates evidence-based priors over fault-tolerant hardware parameters through five surface-code cost models, adds correlated-input sensitivity analysis, and evaluates the resulting intervals with a pre-registered three-layer protocol. Applied to RSA-2048, ECC-256, AES-256, and a Hubbard-model simulation on a superconducting surface-code architecture, it produces three coupled findings that together undermine the single-number convention. Propagating the error rates hardware has actually demonstrated, whose median across five large-array devices is about 4 x 10^-3 rather than the conventional 10^-3, widens the 90% physical-qubit interval to roughly forty times and lifts its median about 4.6 times above the conventional point estimate. Against the same inputs, five independently structured cost models, including two third-party estimators (the Azure Quantum Resource Estimator and Qualtran) that agree with each other to within about ten percent, disagree by a stable factor of two, a structural uncertainty that no single tool reveals. Most consequentially, both third-party estimators exceed their representable envelope for 80 to 100 percent of the evidence-based parameter space, and the AES-256 gate count overflows one tool's counter outright, so these workloads cannot be represented at measured error rates at all. The optimistic convention conceals every one of these effects. The uncertainty machinery we use is standard. The contribution is its evidence-based application, the pre-registered evaluation, and the finding that standard tools break down precisely where hardware operates.
Comments22 pages, 4 figures
