高斯分布
光子
玻色子
采样(信号处理)
物理
量子计算机
算法
统计物理学
探测器
光子计数
贝叶斯概率
计算机科学
量子
量子力学
光学
人工智能
作者
Yu‐Hao Deng,Yi-Chao Gu,Hua-Liang Liu,Si-Qiu Gong,Hao Su,Zhi-Jiong Zhang,Haoyang Tang,Meng-Hao Jia,Jiamin Xu,Ming-Cheng Chen,Jian Qin,Lichao Peng,Jiarong Yan,Yi Hu,Jia Huang,Hao Li,Yuxuan Li,Yaojian Chen,Xiao Jiang,Lin Gan
标识
DOI:10.1103/physrevlett.131.150601
摘要
We report new Gaussian boson sampling experiments with pseudo-photon-number-resolving detection, which register up to 255 photon-click events. We consider partial photon distinguishability and develop a more complete model for the characterization of the noisy Gaussian boson sampling. In the quantum computational advantage regime, we use Bayesian tests and correlation function analysis to validate the samples against all current classical spoofing mockups. Estimating with the best classical algorithms to date, generating a single ideal sample from the same distribution on the supercomputer Frontier would take ∼600 yr using exact methods, whereas our quantum computer, Jiǔzhāng 3.0, takes only 1.27 μs to produce a sample. Generating the hardest sample from the experiment using an exact algorithm would take Frontier∼3.1×10^{10} yr.
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