Sample efficient graph classification using binary Gaussian boson sampling

TL;DR

This paper introduces a simplified quantum machine learning algorithm for graph classification using binary Gaussian boson sampling, which requires less complex hardware and offers efficient feature extraction.

Contribution

The work presents a novel binary GBS-based algorithm for graph classification that is hardware-friendly and connects graph theory with the Torontonian matrix function.

Findings

Binary GBS detectors are simpler and operate at room temperature.

The algorithm effectively extracts features for graph classification.

A connection between graph theory and the Torontonian function is established.

Abstract

We present a variation of a quantum algorithm for the machine learning task of classification with graph-structured data. The algorithm implements a feature extraction strategy that is based on Gaussian boson sampling (GBS) a near term model of quantum computing. However, unlike the currently proposed algorithms for this problem, our GBS setup only requires binary (light/no light) detectors, as opposed to photon number resolving detectors. These detectors are technologically simpler and can operate at room temperature, making our algorithm less complex and less costly to implement on the physical hardware. We also investigate the connection between graph theory and the matrix function called the Torontonian which characterizes the probabilities of binary GBS detection events.