Simulating Gaussian boson sampling quantum computers

TL;DR

This paper reviews phase-space simulation methods for Gaussian boson sampling, aiding the verification of quantum advantage claims in photonic quantum computers by capturing complex quantum correlations.

Contribution

It provides an overview of scalable phase-space simulation techniques for GBS, supporting validation of experimental results and encouraging further theoretical research.

Findings

Phase-space methods are highly scalable for simulating GBS.

These methods can validate experimental outputs across various input states.

They help verify claims of quantum advantage in photonic quantum computing.

Abstract

A growing cohort of experimental linear photonic networks implementing Gaussian boson sampling (GBS) have now claimed quantum advantage. However, many open questions remain on how to effectively verify these experimental results, as scalable methods are needed that fully capture the rich array of quantum correlations generated by these photonic quantum computers. In this paper, we briefly review recent theoretical methods to simulate experimental GBS networks. We focus mostly on methods that use phase-space representations of quantum mechanics, as these methods are highly scalable and can be used to validate experimental outputs and claims of quantum advantage for a variety of input states, ranging from the ideal pure squeezed vacuum state to more realistic thermalized squeezed states. A brief overview of the theory of GBS, recent experiments and other types of methods are also presented. Although this is not an exhaustive review, we aim to provide a brief introduction to phase-space methods applied to linear photonic networks to encourage further theoretical investigations.