Photonic integrated circuits offer a compact and stable platform for generating, manipulating, and detecting light. They are instrumental for classical and quantum applications. Imperfections stemming from fabrication constraints, tolerances, and operation wavelength impose limitations on the accuracy and thus utility of current photonic integrated devices. Mitigating these imperfections typically necessitates a model of the underlying physical structure and the estimation of parameters that are challenging to access. Direct solutions are currently lacking for mesh configurations extending beyond trivial cases. We introduce a scalable and innovative method to characterize photonic chips through an iterative machine learning-assisted procedure. Our method is based on a clear-box approach that harnesses a fully modeled virtual replica of the photonic chip to characterize. The process is sample-efficient and can be carried out with a continuous-wave laser and powermeters. The model estimates individual passive phases, crosstalk, beamsplitter reflectivity values, and relative input/output losses. Building upon the accurate characterization results, we mitigate imperfections to enable enhanced control over the device. We validate our characterization and imperfection mitigation methods on a 12-mode Clements-interferometer equipped with 126 phase shifters, achieving beyond state-of-the-art chip control with an average 99.77% amplitude fidelity on 100 implemented Haar-random unitary matrices.

中文翻译：

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可扩展的机器学习辅助明盒表征，用于最佳控制的光子电路

光子集成电路为产生、操纵和检测光提供了一个紧凑且稳定的平台。它们对于经典和量子应用很有帮助。由于制造限制、公差和工作波长而产生的缺陷对当前光子集成器件的精度和实用性造成了限制。减轻这些缺陷通常需要底层物理结构的模型以及难以访问的参数估计。目前缺乏针对超出简单情况的网格配置的直接解决方案。我们引入了一种可扩展的创新方法，通过迭代机器学习辅助程序来表征光子芯片。我们的方法基于明盒方法，利用光子芯片的完全建模虚拟副本来表征。该过程样品效率高，并且可以使用连续波激光器和功率计进行。该模型估计各个无源相位、串扰、分束器反射率值以及相对输入/输出损耗。基于准确的表征结果，我们减少了缺陷，以增强对设备的控制。我们在配备 126 个移相器的 12 模 Clements 干涉仪上验证了我们的表征和缺陷缓解方法，在 100 个实施的 Haar 随机酉矩阵上实现了超越最先进芯片控制的平均 99.77% 幅度保真度。