Recently, optical neural networks (ONNs) integrated in photonic chips has
received extensive attention because they are expected to implement the same
pattern recognition tasks in the electronic platforms with high efficiency and
low power consumption. However, the current lack of various learning algorithms
to train the ONNs obstructs their further development. In this article, we
propose a novel learning strategy based on neuroevolution to design and train
the ONNs. Two typical neuroevolution algorithms are used to determine the
hyper-parameters of the ONNs and to optimize the weights (phase shifters) in
the connections. In order to demonstrate the effectiveness of the training
algorithms, the trained ONNs are applied in the classification tasks for iris
plants dataset, wine recognition dataset and modulation formats recognition.
The calculated results exhibit that the training algorithms based on
neuroevolution are competitive with other traditional learning algorithms on
both accuracy and stability. Compared with previous works, we introduce an
efficient training method for the ONNs and demonstrate their broad application
prospects in pattern recognition, reinforcement learning and so on.

本文提出了一种使用神经进化学习策略来设计和训练光学神经网络（ONNs）的新方法。通过使用两种典型的神经进化算法来确定 ONNs 的超参数并优化连接中的权重（相移器），本研究证明了该训练算法是有效的，且在对虹膜植物数据集，葡萄酒识别数据集和调制格式识别等分类任务上展示出与其他传统学习算法相当的能力和稳定性。

光子神经网络的高效训练和设计通过神经进化算法实现

Efficient training and design of photonic neural network through  neuroevolution

The difficulty of validating large-scale quantum devices, such as Boson
Samplers, poses a major challenge for any research program that aims to show
quantum advantages over classical hardware. To address this problem, we propose
a novel data-driven approach wherein models are trained to identify common
pathologies using unsupervised machine learning methods. We illustrate this
idea by training a classifier that exploits K-means clustering to distinguish
between Boson Samplers that use indistinguishable photons from those that do
not. We train the model on numerical simulations of small-scale Boson Samplers
and then validate the pattern recognition technique on larger numerical
simulations as well as on photonic chips in both traditional Boson Sampling and
scattershot experiments. The effectiveness of such method relies on
particle-type-dependent internal correlations present in the output
distributions. This approach performs substantially better on the test data
than previous methods and underscores the ability to further generalize its
operation beyond the scope of the examples that it was trained on.

我们提出了一种新的数据驱动方法，将模型训练成使用无监督机器学习方法识别常见病理，以解决验证大规模量子设备（例如波色子采样器）的困难，并且通过训练分类器，该分类器利用 K-means 聚类来区分使用具有相同光子而非使用具有不同光子的波色子采样器。