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
Quantum Engineering Technology Labs, University of Bristol, Bristol, UK.
Ultrafast Quantum Optics Group, Department of Physics, Imperial College London, London, UK.
Sci Adv. 2022 Jan 28;8(4):eabl9236. doi: 10.1126/sciadv.abl9236. Epub 2022 Jan 26.
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.
确定量子机器相对于经典机器具有计算优势的界限，是规划其用途的关键一步。高斯玻色子采样（GBS）是追求量子优势的一种领先方法，其中光子是从高度纠缠的高斯态中测量的。目前运行几分钟的最先进的GBS实验，使用现有的最佳经典算法进行模拟需要6亿年。在此，我们提出了更快的经典GBS模拟方法，包括对回路哈夫尼亚数计算的速度和精度改进。我们在一台约10万个核心的超级计算机上对这些方法进行测试，以模拟多达100个模式和92个光子的GBS实验。这将最先进的GBS实验的模拟时间缩短到了几个月，比之前的估计提高了九个数量级。最后，我们引入了一种分布，从经典角度来看，它可以有效地进行采样，并且通过了各种GBS验证方法。
