Gaussian boson sampling: Benchmarking quantum advantage

TL;DR

This paper introduces a scalable classical algorithm for Gaussian boson sampling that challenges claims of quantum advantage by closely simulating large-scale experiments and analyzing error effects.

Contribution

A highly scalable classical algorithm for GBS is developed, outperforming previous methods and providing insights into how errors impact quantum advantage.

Findings

Classical simulation of GBS up to 1152 modes is more accurate than current experiments.

The new algorithm outperforms all previous classical approximate algorithms.

Errors beyond losses can enable classical simulability of GBS.

Abstract

Quantum computers solve intractable problems which classically require an exponentially long time to compute. With the development of large-scale experiments that claim quantum advantage, a vital issue has now emerged. What are the errors, and how do they affect the complexity of the problem solved? Large-scale Gaussian boson sampling (GBS) experiments give an example in which random numbers are generated. Despite classical hardness, these have computable benchmarks for checking data validity. While there are other quantum computing architectures, Gaussian boson sampling is uniquely testable at all scales. Several large, pioneering quantum computing (QC) experiments have been carried out to investigate quantum advantage. Here, we introduce a highly scalable but classical algorithm that can solve GBS approximately. Our numerical simulation of the output count data is closer to the exact solution than current experiments up to 1152 modes. This algorithm outperforms all previous classical, approximate algorithms and scales efficiently to larger experiments. Our results show that effects beyond losses can cause the errors that allow classical simulability. This work will lead to more precise algorithms and is a step towards understanding how QC quantum advantage is affected by the underlying physics.