AI 中文总结
研究预告式非高斯操作对SU(1,1)干涉仪计量性能的影响，发现单光子 subtraction 等操作在部分透射率区域提升相位信息，但优化相干-压缩分配后其性能仍低于高斯基准，明确了资源约束下非高斯增强与实际精度的边界。
AI 中文摘要
非高斯操作可重塑连续变量探针的光子统计，但只有在一致考虑预告概率和光子数资源时，其计量学优势才有意义。我们在带奇偶检测的平衡SU(1,1)干涉仪中，比较光子 subtracted、光子添加和光子催化作为输入侧预告操作。统一的有限透射率映射提供任意操作阶下的闭合条件矩及对应量子费舍尔信息；内部损耗被吸收为单个有效奇偶可观测量，其无损耗极限恢复理想的回拉测量。在固定制备参数下，单光子 subtraction 和添加在大部分高透射率区域，相较于高斯参考提升了条件相位信息，而多光子催化开辟了有用的低透射率窗口。但当在固定条件探针能量和固定干涉仪增益下，独立优化相干-压缩分配时，三种非高斯操作的成功加权费舍尔信息仍低于优化后的高斯基准。该结论受限于已测试的约束：单光子操作、相干加压缩真空高斯族、固定增益和奇偶读出。光子催化单独生成具有高局域量子费舍尔信息的条件分支，而暗点奇偶对其提取效果差，这识别出是测量不匹配而非态制备失败。该结果在明确陈述的资源约束下，划定了条件非高斯增强与实际可用精度间的清晰边界。
英文摘要
Non-Gaussian operations can reshape the photon statistics of continuous-variable probes, but their metrological advantage is meaningful only when heralding probability and photon-number resources are counted consistently. We compare photon subtraction, photon addition, and photon catalysis as input-side heralding operations in a balanced SU(1,1) interferometer with parity detection. A unified finite-transmissivity map supplies closed conditional moments and the corresponding quantum Fisher information at arbitrary operation order; internal loss is absorbed into a single effective parity observable whose lossless limit recovers the ideal pulled-back measurement. At fixed preparation parameters, single-photon subtraction and addition improve the conditional phase information over the Gaussian reference across most of the high-transmissivity regime, while multi-photon catalysis opens useful low-transmissivity windows. However, when the coherent--squeezed allocation is independently optimized at fixed conditional-probe energy and fixed interferometer gain, the success-weighted Fisher information of all three non-Gaussian operations remains below the optimized Gaussian benchmark. This conclusion is subject to the tested constraints: single-photon operations, a coherent-plus-squeezed-vacuum Gaussian family, fixed gain, and parity readout. Photon catalysis separately generates a conditional branch with high local quantum Fisher information that dark-point parity extracts poorly, identifying a measurement mismatch rather than a state-preparation failure. The result draws a sharp boundary between conditional non-Gaussian enhancement and practically available precision under explicitly stated resource constraints.
Comments25 pages, 10 figures
