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Optimization of distributed EPR entanglement generated between two Gaussian fields by the modified steepest descent method

机译:修改近近血管下降方法在两个高斯字段之间产生的分布式EPR纠缠优化

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Recent theoretical investigations on quantum coherent feedback networks have found that with the same pump power, the Einstein-Podolski-Rosen (EPR)-like entanglement generated via a dual nondegenerate optical parametric amplifier (NOPA) system placed in a certain coherent feedback loop is stronger than the EPR-like entangled pairs produced by a single NOPA. In this paper, we present a linear quantum system consisting of two NOPAs and a static linear passive network of optical devices. The network has six inputs and six outputs, among which four outputs and four inputs are connected in a coherent feedback loop with the two NOPAs. This passive network is represented by a 6 × 6 complex unitary matrix. A modified steepest descent method is used to find a passive complex unitary matrix at which the entanglement of this dual-NOPA network is locally maximized. Here we choose the matrix corresponding to a dual-NOPA coherent feedback network from our previous work as a starting point for the modified steepest descent algorithm. By decomposing the unitary matrix obtained by the algorithm as the product of so-called two-level unitary matrices, we find an optimized configuration in which the complex matrix is realized by a static optical network made of beam splitters.
机译:最近对量子相干反馈网络的理论研究已经发现,利用相同的泵浦功率,通过置于某个相干反馈回路中的双非不合物光学参数(NoPA)系统产生的Einstein-Podolski-Rosen(EPR) - 匹配缠结更强比单个Nopa制作的epr样缠结的对。在本文中,我们介绍了由两个NOPA和光学装置的静态线性被动网络组成的线性量子系统。网络具有六个输入和六个输出,其中四个输出和四个输入连接在两个NOPA的相干反馈循环中。该被动网络由6×6复杂的酉矩阵表示。修改的速度下降方法用于找到被动复合酉矩阵,其局部最大化该双NoPA网络的缠结。在这里,我们选择与我们之前的工作中的双NoPA相干反馈网络相对应的矩阵作为修改的速度下降算法的起点。通过将算法获得的单一矩阵分解为所谓的两级酉矩阵的乘积,我们找到了一种优化的配置,其中通过横梁分离器制成的静态光网络实现了复杂矩阵。

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