AI 中文总结
针对可编程光子集成电路校准和控制问题，提出通用框架，采用串联神经网络结合架构感知采样、优化采样两种新策略，实验验证可解决采样偏差，用于相干检测时能精确控制幅度和相位，提升光子神经网络任务表现。
AI 中文摘要
高效校准和控制可编程光子集成电路是扩展量子和经典光学计算处理器的基础。基于神经网络的模型提供了与架构无关的解决方案，但现有方法因控制信号集与光学响应之间的多对一映射问题以及来自均匀电流采样的有偏差训练数据集，学习和泛化能力有限。本文提出了一个通用校准和控制框架，采用串联神经网络并结合两种新的数据生成策略：基于哈尔测度原理的架构感知采样和利用差分进化的与物理无关的优化采样。我们在3x3和4x4相干MZI网格上对这些方法进行了实验验证，证明我们的方法解决了先前工作中固有的采样偏差。当使用随机酉矩阵评估时，我们的解决方案比标准均匀采样基线的精度高出约2比特。此外，我们通过实验将该框架的应用扩展到相干检测，实现了对幅度和相位的精确控制，并验证了其对光子神经网络任务的影响。
英文摘要
Efficient calibration and control of programmable photonic integrated circuits are fundamental for scaling quantum and classical optical computing processors. While neural network-based models offer an architecture-agnostic solution, existing approaches suffer from limited learning and generalization capabilities due to the many-to-one mapping problem between sets of control signals and optical responses, and biased training datasets derived from uniform current sampling. In this work, we propose a universal calibration and control framework employing tandem neural networks combined with two novel data generation strategies: architecture-aware sampling based on Haar measure principles, and optimized sampling, a physics-agnostic approach utilizing differential evolution. We experimentally validate these methods on 3x3 and 4x4 coherent MZI meshes, demonstrating that our approach addresses the sampling bias inherent in previous works. When evaluated using random unitary matrices, our solution outperforms standard uniform sampling baselines by ~2 bits of precision. Furthermore, we experimentally extend the application of this framework to coherent detection, achieving precise control over both amplitude and phase, and validate its impact on photonic neural network tasks.
