通过改进最陡下降法优化两个高斯场间生成的分布式EPR纠缠
Optimization of distributed EPR entanglement generated between two Gaussian fields by the modified steepest descent method
AI总结：
本文通过改进最陡下降法优化双NOPA网络的EPR纠缠，利用被动线性光子网络实现最优配置。
摘要：
最近关于量子相干反馈网络的理论研究发现，相同泵浦功率下，双非退化光学参量放大器（NOPA）系统置于特定相干反馈环路中生成的EPR类纠缠强于单NOPA生成的EPR类纠缠对。本文提出由两个NOPA和一个静态线性光子网络组成的线性量子系统，该网络有六个输入和六个输出，其中四个输出和四个输入与两个NOPA构成相干反馈环路。该被动网络由一个6×6复数酉矩阵表示。采用改进的最陡下降法寻找使该双NOPA网络纠缠局部最大的被动复数酉矩阵。本文选取之前工作中双NOPA相干反馈网络对应的矩阵作为改进最陡下降算法的初始点。通过将算法获得的酉矩阵分解为所谓的两级酉矩阵的乘积，找到一个优化配置，其中复数矩阵通过由光束分离器组成的静态光学网络实现。
英文摘要：
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 $6 \times 6$ 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.
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