AI 中文总结
本研究提出以货币化延迟成本统一量化后量子迁移与经典风险，构建竞争风险模型并证明排序最优性，通过模拟验证，为资产迁移优先级提供决策依据。
AI 中文摘要
组织在决定首先将哪些资产迁移到后量子密码学，以及该工作如何与经典发现的积压竞争时，缺乏一个共同的单位：后量子评分是无量纲且仅针对量子的，而经典风险是年化损失。我们将这两种威胁表示为同一资产上以货币计量的每年延迟成本。量子项是延迟迁移导致不可逆的先收集后解密损失的速率，根据所需的保密持续时间、可记录流量份额和数据周转率计算，使用以闭合形式拟合到已发表专家锚点的量子到达定律。一个竞争风险因子将这两项耦合起来，使得同一损失不会被重复计算。我们证明了该构造不是经典和量子评分的加权和，给出了一个独立于资产价值的经典危害优势阈值，并表明在恒定损失率下，所诱导的顺序对于工作排序是最优的。我们通过模拟、自适应求积和第二个实现来验证该实现。在四个公共系统上，使用从公共文档中假设的参数，量子项仅对一个系统（信噪比1.36对比0.14-0.46）重新排序了超出输入不确定性的资产列表，明确移动了特定的长寿命、安静、可记录的资产，否则仅改变幅度。资产价值和周转率解释了量子项中73-91%的不确定性；到达日期解释了3-17%。案例研究是说明性的，并未针对结果进行验证。
英文摘要
Organisations deciding which assets to migrate to post-quantum cryptography first, and how that work competes with a backlog of classical findings, lack a common unit: post-quantum scores are dimensionless and quantum-only, while classical risk is annualised loss. We express both threats as a cost of delay in currency per year on the same asset. The quantum term is the rate at which deferring migration commits irreversible harvest-now-decrypt-later loss, computed from required confidentiality duration, recordable traffic share and data turnover, using a quantum-arrival law fitted in closed form to published expert anchors. A competing-risks factor couples the two terms so the same loss is not counted twice. We prove that the construction is not a weighted sum of a classical and a quantum score, give a dominance threshold on the classical hazard that is independent of asset value, and show that the induced order is optimal for sequencing work under constant loss rates. We verify the implementation by simulation, adaptive quadrature and a second implementation. On four public systems with parameters assumed from public documentation, the quantum term reorders the asset list beyond input uncertainty for one system only (signal-to-noise 1.36 against 0.14-0.46), moves specific long-lived, quiet, recordable assets decisively, and otherwise changes magnitudes. Asset value and turnover explain 73-91% of the uncertainty in the quantum term; the arrival date explains 3-17%. The case studies are illustrative and are not validated against outcomes.
Comments17 pages, 9 figures, 4 tables. Code and case studies: https://github.com/AnimeshShaw/pqc-cost-of-delay ; archive: https://doi.org/10.5281/zenodo.23133468
