Benchmarking of Gaussian boson sampling using two-point correlators

TL;DR

This paper develops a framework for benchmarking Gaussian boson sampling by analyzing two-point correlators, enabling the distinction of quantum from classical interference even with experimental imperfections.

Contribution

It introduces a method to identify quantum interference signatures in Gaussian states using two-point correlators and moments, accounting for realistic experimental limitations.

Findings

Quantum interference signatures can be distinguished using two-point correlators.

The method remains effective under loss, noise, and finite photon resolution.

Benchmarking framework applicable to current experimental setups.

Abstract

Gaussian boson sampling is a promising scheme for demonstrating a quantum computational advantage using photonic states that are accessible in a laboratory and, thus, offer scalable sources of quantum light. In this contribution, we study two-point photon-number correlation functions to gain insight into the interference of Gaussian states in optical networks. We investigate the characteristic features of statistical signatures which enable us to distinguish classical from quantum interference. In contrast to the typical implementation of boson sampling, we find additional contributions to the correlators under study which stem from the phase dependence of Gaussian states and which are not observable when Fock states interfere. Using the first three moments, we formulate the tools required to experimentally observe signatures of quantum interference of Gaussian states using two outputs only. By considering the current architectural limitations in realistic experiments, we further show that a statistically significant discrimination between quantum and classical interference is possible even in the presence of loss, noise, and a finite photon-number resolution. Therefore, we formulate and apply a theoretical framework to benchmark the quantum features of Gaussian boson sampling under realistic conditions.