In this section
@article{Bulmer:2022:10.1126/sciadv.abl9236,
author = {Bulmer, JFF and Bell, BA and Chadwick, RS and Jones, AE and Moise, D and Rigazzi, A and Thorbecke, J and Haus, U-U and Van, Vaerenbergh T and Patel, RB and Walmsley, IA and Laing, A},
doi = {10.1126/sciadv.abl9236},
journal = {Science Advances},
title = {The boundary for quantum advantage in Gaussian boson sampling},
url = {http://dx.doi.org/10.1126/sciadv.abl9236},
volume = {8},
year = {2022}
}
TY - JOUR
AB - 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 GBS experiments that run in minutes would require 600 million years to simulate using the best preexisting classical algorithms. Here, we present faster classical GBS simulation methods, including speed and accuracy improvements to the calculation of loop hafnians. We test these on a ∼100,000-core supercomputer to emulate GBS experiments with up to 100 modes and up to 92 photons. This reduces the simulation time for state-of-the-art GBS experiments to several months, a nine–orders of magnitude improvement over previous estimates. Last, we introduce a distribution that is efficient to sample from classically and that passes a variety of GBS validation methods
AU - Bulmer,JFF
AU - Bell,BA
AU - Chadwick,RS
AU - Jones,AE
AU - Moise,D
AU - Rigazzi,A
AU - Thorbecke,J
AU - Haus,U-U
AU - Van,Vaerenbergh T
AU - Patel,RB
AU - Walmsley,IA
AU - Laing,A
DO - 10.1126/sciadv.abl9236
PY - 2022///
SN - 2375-2548
TI - The boundary for quantum advantage in Gaussian boson sampling
T2 - Science Advances
UR - http://dx.doi.org/10.1126/sciadv.abl9236
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000747329300021&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - https://www.science.org/doi/10.1126/sciadv.abl9236
VL - 8
ER -