利用不定因果序实现多参数估计超越高斯最优
Surpassing Gaussian optimality in multiparameter estimation with indefinite causal order
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中文总结 AI 辅助
该研究确定连续变量量子系统多参数估计的最优高斯探针，发现不定因果序可超越高斯精度界限，其优势独立于非高斯性，能提升估计精度。
中文摘要 AI 辅助
我们确定了单模高斯探针，其通过对真空态执行位移和压缩操作生成，是连续变量量子系统中位移与压缩操作同时估计的最优探针。重要的是，结果表明固定能量下的最佳精度并非由实验成本高昂的压缩资源实现，而是通过将部分能量转向位移来达成，从而实现更具资源效率的操作。此外，在探针制备或参数编码步骤中引入不定因果序（ICO）可超越高斯精度界限，即便最优高斯探针态对操作顺序未知。具体而言，我们观察到两种确定序的奇宇称叠加态在特定参数区域可提升优于最优高斯探针的精度。进一步而言，该优势不能仅归因于非高斯性（由非高斯性相对熵量化），凸显ICO是增强多参数估计的独立资源。
英文摘要
We identify single-mode Gaussian probes, generated by displacement and squeezing operations on the vacuum state, which are optimal for the simultaneous estimation of displacement and squeezing operations in continuous-variable quantum systems. Importantly, our results reveal that the best precision at a fixed energy is achieved not by an experimentally costly squeezing resource, but rather by redirecting some of the energy towards displacement, thus allowing for more resource-effective operations. Furthermore, introducing indefinite causal order (ICO) in either the probe preparation or parameter encoding step can surpass the Gaussian precision bound, even though the optimal Gaussian probe state is agnostic to the ordering of the operations. Specifically, we observe that odd-parity superpositions of the two definite orders can enhance precision over optimal Gaussian probes in specific parameter regimes. Further, the observed advantage cannot be attributed solely to non-Gaussianity, as quantified by the relative entropy of non-Gaussianity, highlighting ICO as an independent resource for enhancing multiparameter estimation.
