Towards graph classification with Gaussian Boson Sampling by embedding graphs on the X8 photonic chip

TL;DR

This paper explores using the X8 photonic chip for graph classification by embedding graphs into quantum states via Gaussian Boson Sampling, comparing experimental and classical results.

Contribution

It demonstrates the potential of the X8 photonic chip for graph embedding and classification, bridging quantum photonics and graph analysis.

Findings

Experimental results on the X8 chip show promising graph embedding capabilities.

Comparison with classical simulations highlights quantum advantages and limitations.

Photon loss impacts embedding quality and classification accuracy.

Abstract

Photonics chips on which one can perform Gaussian Boson Sampling have become accessible on the cloud, in particular the X8 chip of Xanadu. In this technical report, we study its potential use as a first step towards graph classification on quantum devices. In order to achieve this goal, we study the generated samples of the graph embedding method which leads to feature vectors. This is done on a restricted class of unweighted, undirected and loop-free graphs. Hardware constraints are matched to properties of graphs that can be encoded. We report experiments on the X8 chip as well as comparisons to numerical simulations on a classical computer and analytical solutions. We conclude this technical report by trying to take photon loss into account and explain the observed results accordingly.