Simulating Gaussian boson sampling on graphs in polynomial time

TL;DR

This paper demonstrates that certain distributions associated with Gaussian Boson Sampling on graphs can be efficiently simulated classically in polynomial time, challenging the presumed quantum advantage for these applications.

Contribution

It introduces polynomial-time classical algorithms for sampling distributions related to Gaussian Boson Sampling on graphs, questioning their quantum speedup.

Findings

Classical polynomial-time sampling of GBS-related distributions is possible.

Quantum advantage may not apply to all GBS graph applications.

Some Boson sampling distributions are classically simulatable.

Abstract

We show that a distribution related to Gaussian Boson Sampling (GBS) on graphs can be sampled classically in polynomial time. Graphical applications of GBS typically sample from this distribution, and thus quantum algorithms do not provide exponential speedup for these applications. We also show that another distribution related to Boson sampling can be sampled classically in polynomial time.