可重构光学干涉仪程序设计的结构无关算法
Architecture agnostic algorithm for reconfigurable optical interferometer programming
摘要：
我们发展了学习算法来建立可重构光学干涉仪的结构无关模型。对干涉仪的光学模式的酉变换进行编程或者遵循给出相移集合的酉矩阵的解析表达式，或者如果解析分解不存在则需要优化例程。我们的算法采用监督学习策略，该策略将干涉仪的模型与由所研究的设备产生的样本填充的训练集相匹配。简单的优化例程使用训练的模型来输出具有对应于期望的酉变换的给定架构的干涉仪的相移。我们的研究结果提供了有效的调谐干涉仪的配方，即使没有严格的分析描述，这打开了机会，探索新的架构的干涉电路。
英文摘要：
We develop the learning algorithm to build the architecture agnostic model of the reconfigurable optical interferometer. Programming the unitary transformation on the optical modes of the interferometer either follows the analytical expression yielding the unitary matrix given the set of phaseshifts or requires the optimization routine if the analytic decomposition does not exist. Our algorithm adopts the supervised learning strategy which matches the model of the interferometer to the training set populated by the samples produced by the device under study. The simple optimization routine uses the trained model to output the phaseshifts of the interferometer with the given architecture corresponding to the desired unitary transformation. Our result provides the recipe for efficient tuning of the interferometers even without rigorous analytical description which opens opportunity to explore new architectures of the interferometric circuits.
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