Classical benchmarking of Gaussian Boson Sampling on the Titan supercomputer

TL;DR

This paper benchmarks the classical simulation of Gaussian Boson Sampling on the Titan supercomputer, demonstrating the feasibility of simulating large systems and analyzing their performance in graph problems.

Contribution

It presents the first large-scale classical simulation of GBS with threshold detectors on Titan, providing resource estimates and benchmarking GBS for dense subgraph identification.

Findings

Simulated 800 modes with 20 clicks in about two hours using Titan.

Large photon losses can be tolerated in GBS simulations.

Threshold detectors outperform photon-number-resolving detectors in certain tasks.

Abstract

Gaussian Boson Sampling is a model of photonic quantum computing where single-mode squeezed states are sent through linear-optical interferometers and measured using single-photon detectors. In this work, we employ a recent exact sampling algorithm for GBS with threshold detectors to perform classical simulations on the Titan supercomputer. We determine the time and memory resources as well as the amount of computational nodes required to produce samples for different numbers of modes and detector clicks. It is possible to simulate a system with 800 optical modes postselected on outputs with 20 detector clicks, producing a single sample in roughly two hours using $40\%$ of the available nodes of Titan. Additionally, we benchmark the performance of GBS when applied to dense subgraph identification, even in the presence of photon loss. We perform sampling for several graphs containing as many as 200 vertices. Our findings indicate that large losses can be tolerated and that the use of threshold detectors is preferable over using photon-number-resolving detectors postselected on collision-free outputs.