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  • The boundary for quantum advantage in Gaussian boson sampling

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    Patel507326-Published.pdf (536.1Kb)
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    Version of Record (VoR)
    Author(s)
    Bulmer, Jacob FF
    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
    Griffith University Author(s)
    Patel, Raj B.
    Year published
    2022
    Metadata
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    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 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 ...
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    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.
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    Journal Title
    Science Advances
    Volume
    8
    Issue
    4
    DOI
    https://doi.org/10.1126/sciadv.abl9236
    Copyright Statement
    © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).
    Subject
    Quantum computation
    Science & Technology
    Multidisciplinary Sciences
    Science & Technology - Other Topics
    PERFORMANCE
    quant-ph
    Publication URI
    http://hdl.handle.net/10072/414475
    Collection
    • Journal articles

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