Universal unitary photonic devices can apply arbitrary unitary transformations to a vector of input modes and provide a promising hardware platform for fast and energy-efficient machine learning using light. We simulate the gradient-based optimization of random unitary matrices on universal photonic devices composed of imperfect tunable interferometers. If device components are initialized uniform-randomly, the locally-interacting nature of the mesh components biases the optimization search space towards banded unitary matrices, limiting convergence to random unitary matrices. We detail a procedure for initializing the device by sampling from the distribution of random unitary matrices and show that this greatly improves convergence speed. We also explore mesh architecture improvements such as adding extra tunable beamsplitters or permuting waveguide layers to further improve the training speed and scalability of these devices.
翻译:通用单一光度装置可以对输入模式的矢量应用任意的单一转换,并为利用光学进行快速和节能的机器学习提供一个有希望的硬件平台。我们模拟由不完善的金枪鱼可探测干涉仪组成的通用光度装置随机单一矩阵的梯度优化。如果设备部件初始化为统一的随机,网状部件的局部相互作用性质使优化搜索空间偏向带宽的单一矩阵,限制对随机单一矩阵的趋同。我们从随机单一矩阵的分布中抽取一个启动装置的程序,并表明这大大加快了聚合速度。我们还探索网状结构的改进,例如增加额外的金枪鱼试样或移动波导层,以进一步提高这些装置的培训速度和可缩放性。