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The boundary for quantum advantage in Gaussian boson sampling

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 pur...

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Detalles Bibliográficos
Autores principales: Bulmer, Jacob F. F., Bell, Bryn A., Chadwick, Rachel S., Jones, Alex E., Moise, Diana, Rigazzi, Alessandro, Thorbecke, Jan, Haus, Utz-Uwe, Van Vaerenbergh, Thomas, Patel, Raj B., Walmsley, Ian A., Laing, Anthony
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791606/
https://www.ncbi.nlm.nih.gov/pubmed/35080972
http://dx.doi.org/10.1126/sciadv.abl9236
Descripción
Sumario: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.