Title: Pattern recognition techniques for Boson Sampling validation,
Abstract: 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.