Pattern recognition techniques for Boson Sampling validation
This addresses a major challenge in quantum computing research for validating quantum advantages, though it is incremental as it builds on existing validation methods.
The paper tackles the problem of validating large-scale quantum devices like Boson Samplers by proposing a data-driven approach using unsupervised machine learning to identify pathologies, achieving substantially better performance on test data than previous methods.
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.