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。

Boson Sampling 验证的模式识别技术