AI 中文总结
该研究提出通用单次射击保真度度量，可直接量化单次测量中正确识别光子数的概率，能区分光子损失与误识别，实现不同架构光子数分辨探测器的定量比较，为光子量子计算相关探测器评估提供统一基准。
AI 中文摘要
光子数分辨（PNR）探测器对光子量子计算至关重要，其中单个测量结果必须可靠地预示特定量子态。然而，探测器保真度传统上通过大量测量得到的集合平均统计量来评估，即便单个光子数分配频繁出错，该统计量仍可能保持较高水平。本文提出一种通用单次射击保真度，直接量化单次测量中正确识别光子数的概率。该框架结合基于效率的正算子值测度（POVM）与由探测器响应导出的分辨率驱动混淆矩阵，可将光子损失与光子数误识别分开处理，再合并为单一可操作度量。这种区分揭示了两种根本不同的限制：探测效率损失代表不可恢复的硬件约束，而分辨率驱动的误识别可通过引入弃权（不执行）区域降低，以生成率换取置信度。由于该度量的定义独立于探测器架构，它能在同一基准上对能量分辨探测器（如过渡-edge传感器）与多路复用点击式探测器进行直接比较。将该框架应用于三种不同探测器架构的校准数据，我们展示了对离散变量与连续变量光子量子计算相关的光子数区间的定量比较。所得基准为评估光子数分辨探测器提供了通用可操作度量，并将探测器性能与光子量子计算需求关联起来。
英文摘要
Photon-number-resolving (PNR) detectors are essential for photonic quantum computing, where a single measurement outcome must reliably herald a specific quantum state. However, detector fidelity is conventionally evaluated using ensemble-averaged statistics obtained from many measurements, which can remain high even when individual photon-number assignments are frequently misidentified. Here we introduce a universal single-shot fidelity that directly quantifies the probability of correctly identifying a photon number in a single measurement. The framework combines an efficiency-based POVM with a resolution-driven confusion matrix derived from the detector response, allowing photon loss and photon-number misidentification to be treated separately and then recombined into a single operational metric. This distinction reveals two fundamentally different limitations. Detection-efficiency loss represents an unrecoverable hardware constraint, whereas resolution-driven misidentification can be reduced by introducing a rejection region, trading generation rate for confidence. Because the metric is defined independently of detector architecture, it enables direct comparison between energy-resolving detectors such as transition-edge sensors and multiplexed click-based detectors on the same footing. Applying the framework to calibration data from three distinct detector architectures, we demonstrate quantitative comparison across photon-number regimes relevant to both discrete-variable and continuous-variable photonic quantum computing. The resulting benchmark provides a common operational metric for evaluating photon-number-resolving detectors and connecting detector performance to photonic quantum-computing requirements.
