The Boundary for Quantum Advantage in Gaussian Boson Sampling

TL;DR

This paper advances classical simulation methods for Gaussian Boson Sampling, significantly reducing simulation time and establishing a new benchmark for quantum advantage boundaries in photonic quantum computing.

Contribution

It introduces faster classical algorithms for GBS simulation, enabling more accurate and efficient emulation of large-scale experiments, and proposes a new classical distribution for validation.

Findings

Classical simulation of GBS experiments is now feasible in months instead of years.

The improved methods can simulate experiments with up to 100 modes and 92 photons.

A new classical distribution passes GBS validation tests, challenging quantum advantage claims.

Abstract

Identifying the boundary beyond which quantum machines provide a computational advantage over their classical counterparts is a crucial step in charting their usefulness. Gaussian Boson Sampling (GBS), in which photons are measured from a highly entangled Gaussian state, is a leading approach in pursuing quantum advantage. State-of-the-art quantum photonics experiments that, once programmed, run in minutes, would require 600 million years to simulate using the best pre-existing classical algorithms. Here, we present substantially faster classical GBS simulation methods, including speed and accuracy improvements to the calculation of loop hafnians, the matrix function at the heart of GBS. We test these on a $\sim \! 100,000$ core supercomputer to emulate a range of different GBS experiments with up to 100 modes and up to 92 photons. This reduces the run-time of classically simulating state-of-the-art GBS experiments to several months -- a nine orders of magnitude improvement over previous estimates. Finally, we introduce a distribution that is efficient to sample from classically and that passes a variety of GBS validation methods, providing an important adversary for future experiments to test against.