论文标题
高度精确的高斯工艺层析成像，具有几何形式的连贯状态
Highly accurate Gaussian process tomography with geometrical sets of coherent states
论文作者
论文摘要
我们提出了一种选择的实用策略，以选择一组输入相干状态，这些状态几乎是最佳的，用于重建单模高斯量子过程，并使用输出状态杂种测量值。我们首先得出平均平方纠正的分析表达式，该表达式量化了通用过程断层扫描和大数据的重建精度。使用这样的表达式，在放宽痕量保留约束时，我们引入了一组错误的输入相干状态集，该状态与测量数据或未知的真实过程 - 几何集合。我们从数值上表明，从这种输入相干状态进行的过程重建几乎与从最佳的一组相干状态中所选择的完整知识所选择的一组相干状态一样准确。这使我们能够有效地表征高斯流程，即使具有相当低的能量相干状态。我们从数值上观察到，没有痕量保存的几何策略都会击败所有非自适应策略，即只要位移组件不太大，典型参数的任意痕量保护高斯过程范围范围。
We propose a practical strategy for choosing sets of input coherent states that are near-optimal for reconstructing single-mode Gaussian quantum processes with output-state heterodyne measurements. We first derive analytical expressions for the mean squared-error that quantifies the reconstruction accuracy for general process tomography and large data. Using such expressions, upon relaxing the trace-preserving constraint, we introduce an error-reducing set of input coherent states that is independent of the measurement data or the unknown true process -- the geometrical set. We numerically show that process reconstruction from such input coherent states is nearly as accurate as that from the best possible set of coherent states chosen with the complete knowledge about the process. This allows us to efficiently characterize Gaussian processes even with reasonably low-energy coherent states. We numerically observe that the geometrical strategy without trace preservation beats all nonadaptive strategies for arbitrary trace-preserving Gaussian processes of typical parameter ranges so long as the displacement components are not too large.
