发表机构
SUPCON Technology; College of Artificial Intelligence, Zhejiang University（中控技术; 浙江大学人工智能学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究将物理访问纳入量子数据估值，建立其与夏普利值的联系，证明有限副本量子数据的估值依赖于物理访问，相同量子样本在不同访问模型下估值与排名不同。
AI 中文摘要
数据估值研究如何将学习效用归因于训练数据贡献者。大多数经典数据估值方案在数据成为可重用记录后才展开，此时数据的物理读出已基本固定。有限副本量子数据则不同：未知量子态是可消耗的物理系统，相同的输入量子态和下游任务在不同物理访问模型下会产生不同的夏普利值。本框架将物理访问本身视为量子数据估值的组成部分，明确了这种依赖关系。我们建立了物理访问优势与贡献者层面数据估值之间的精确联系；对于嵌套访问模型，证明了更丰富的物理访问所带来的最大下游效用增益，恰好决定了最大对称夏普利排名反转边际。更一般地，对于任意访问模型对（包括非嵌套对），我们推导了完整夏普利归因向量可能发生偏移的精确几何表征。对于固定学习流水线，我们进一步获得了有限副本估值的可操作夏普利可观测表示。数值实验表明，仅改变物理访问模型，相同量子样本可获得不同的估值和排名。这些结果表明，量子数据价值并非仅由 underlying 态本身决定，而是由量子态、物理访问与下游学习任务的相互作用产生。
英文摘要
Data valuation asks how learning utility should be attributed to training data contributors. Most classical formulations begin after data have become reusable records, so the physical readout of the data is effectively fixed. Finite-copy quantum data are different: unknown states are consumable physical systems, and the same supplied states and downstream task can yield different Shapley values under different physical access models. Our framework makes this dependence explicit by treating physical access as a component of quantum data valuation itself. We establish an exact connection between physical-access advantage and contributor-level data valuation. For nested access models, we prove that the maximal downstream utility gain enabled by richer physical access exactly determines the largest symmetric Shapley ranking-reversal margin. More generally, for arbitrary access-model pairs, including non-nested ones, we derive an exact geometric characterization of the possible shifts of the full Shapley attribution vector. For fixed learning pipelines, we further obtain an operational Shapley-observable representation for finite-copy valuation. Numerical experiments demonstrate that identical quantum samples can receive different values and rankings when only the physical access model is changed. These results establish that quantum data value is not an intrinsic property of the underlying states alone, but emerges from the interaction between quantum states, physical access, and the downstream learning task.
