A fast and frugal Gaussian Boson Sampling emulator

TL;DR

This paper presents a highly efficient classical algorithm that simulates Gaussian Boson Sampling experiments with over one hundred modes, outperforming previous methods and requiring minimal computational resources.

Contribution

The authors introduce a novel classical simulation method for Gaussian Boson Sampling that surpasses previous benchmarks using only a single CPU or GPU, with potential for broader applications.

Findings

Outperforms existing Gaussian Boson Sampling simulations on 100+ modes

Achieves high sampling rates with minimal computational resources

Potentially applicable to qubit-based sampling and classical-quantum algorithms

Abstract

If classical algorithms have been successful in reproducing the estimation of expectation values of observables of some quantum circuits using off-the-shelf computing resources, matching the performance of the most advanced quantum devices on sampling problems usually requires extreme cost in terms of memory and computing operations, making them accessible to only a handful of supercomputers around the world. In this work, we demonstrate for the first time a classical simulation outperforming Gaussian boson sampling experiments of one hundred modes on established benchmark tests using a single CPU or GPU. Being embarrassingly parallelizable, a small number of CPUs or GPUs allows us to match previous sampling rates that required more than one hundred GPUs. We believe algorithmic and implementation improvements will generalize our tools to photo-counting, single-photon inputs, and pseudo-photon-number-resolving scenarios beyond one thousand modes. Finally, most of the innovations in our tools remain valid for generic probability distributions over binary variables, rendering it potentially applicable to the simulation of qubit-based sampling problems and creating classical surrogates for classical-quantum algorithms.