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arXiv 2602.20701physics.optics

可编程光子网格上的原生QR分解

Native QR Factorization on Programmable Photonic Meshes

S. A. Fldzhyan, S. S. Straupe, M. Yu. Saygin

AI总结:

本文提出了一种基于可编程单元光子干涉仪网格的原生光子QR分解方法,通过局部功率路由步骤实现矩阵分解,具有更低的运算复杂度,适用于迭代谱计算和预处理SVD流程。

摘要:

我们提出了一种利用可编程单元光子干涉仪网格计算矩阵QR分解的光子原生方法。该方法通过可调双模干涉元件中的局部功率路由步骤配置网格,并直接从光学输出读取上三角因子。运算数量随矩阵大小N增长为O(N log₂N),比标准数字QR方法的三次方复杂度更优。此外,相同架构可通过镜像排列的干涉仪支持迭代谱计算,同时描述了用于Hessenberg降阶和双对角化相关的光子过程,作为QR和SVD流程的兼容预处理步骤。还提供了与流式阵列计算架构的比较。本方法在阻塞QR分解的渐近复杂度上与之相当,但在Hessenberg降阶和双对角化中更高效。

英文摘要:

We propose a photonic native procedure for computing the QR factorization of a matrix using a programmable unitary interferometer mesh. The method configures the mesh through a sequence of local power routing steps within tunable two mode interferometric elements, while reading out the resulting upper triangular factor directly from the optical outputs. The number of physical operations grows as $ O(N\log_2N)$ with matrix size $N$, reducing the runtime relative to standard digital QR routines, which scale cubically ($O(N^3)$). Beyond single factorizations, the same architecture supports iterative spectral computations by reusing the configured interferometer in a mirrored arrangement that implements the core update step of the QR eigenvalue algorithm. We also describe related optical procedures for Hessenberg reduction and bidiagonalization, serving as compatible preprocessors for QR and SVD workflows. A comparison with the systolic array computational architecture is provided. Our approach exhibits comparable asymptotic complexity for blocked QR decomposition and is more efficient for Hessenberg reduction and bidiagonalization.

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