Estimating the Percentage of GBS Advantage in Gaussian Expectation Problems

TL;DR

This paper improves algorithms using Gaussian Boson Sampling to estimate Gaussian expectations, optimizing photon number to expand the problem space where these methods outperform classical Monte Carlo, often achieving near-complete advantage.

Contribution

It introduces optimized GBS algorithms with enhanced performance estimates, significantly increasing the problem space where quantum sampling outperforms classical methods.

Findings

Algorithms outperform Monte Carlo in a substantial portion of the problem space.

Optimizing photon number enhances the advantage of GBS algorithms.

Near-100% advantage in certain special cases.

Abstract

Gaussian Boson Sampling (GBS), which can be realized with a photonic quantum computing model, perform some special kind of sampling tasks. In [4], we introduced algorithms that use GBS samples to approximate Gaussian expectation problems. We found a non-empty open subset of the problem space where these algorithms achieve exponential speedup over the standard Monte Carlo (MC) method. This speedup is defined in terms of the guaranteed sample size to reach the same accuracy $\epsilon$ and success probability $\delta$ under the $(\epsilon, \delta)$ multiplicative error approximation scheme. In this paper, we enhance our original approach by optimizing the average photon number in the GBS distribution to match the specific Gaussian expectation problem. We provide updated estimates of the guaranteed sample size for these improved algorithms and quantify the proportion of problem space where they outperform MC. Numerical results indicate that the proportion of the problem space where our improved algorithms have an advantage is substantial, and the advantage gained is significant. Notably, for certain special cases, our methods consistently outperform MC across nearly 100\% of the problem space.