AI 中文总结
本文刻画了满足完美局部隐私的高斯量子网络，确定了达海森堡标度的最优高斯探针，验证了其超越散粒噪声极限的量子增强灵敏度，为构建隐私保护型连续变量量子传感网络提供通用框架。
AI 中文摘要
私有分布式量子传感旨在估计授权的集体参数，同时防止对各个局部参数进行独立估计。本文分析性地刻画了满足完美局部隐私条件的高斯量子网络。利用高斯配对矩阵的图表示，我们证明了双模压缩真空态构成了保护隐私的高斯态的基本构建块。随后，我们分析性地推导出完美局部隐私下的最优灵敏度，并确定了一种高斯探针，其相对于光子数和传感模式数均达到海森堡标度。利用本地零差测量和最大似然估计，我们数值验证了其超越散粒噪声极限的量子增强灵敏度，同时保持了完美的局部隐私。我们进一步证明，局部隐私条件在任意与相位无关的量子信道（包括光学损耗）下均能保持。我们的结果为构建和优化保护隐私的连续变量量子传感网络提供了通用框架。
英文摘要
Private distributed quantum sensing aims to estimate an authorized collective parameter while preventing independent estimation of individual local parameters. Here, we analytically characterize Gaussian quantum networks satisfying this perfect local privacy condition. Using a graph representation of the Gaussian pairing matrix, we show that two-mode squeezed vacuum states constitute the essential building blocks of privacy-preserving Gaussian states. We then analytically derive the optimal sensitivity under perfect local privacy and determine a Gaussian probe that achieves Heisenberg scaling with respect to both the photon number and the number of sensing modes. Using local homodyne measurements and maximum-likelihood estimation, we numerically verify quantum-enhanced sensitivity beyond the shot-noise limit while preserving perfect local privacy. We further show that the local-privacy condition is preserved under arbitrary phase-independent quantum channels, including optical loss. Our results provide a general framework for constructing and optimizing privacy-preserving continuous-variable quantum sensing networks.
