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Gaussian Boson Sampling with Pseudo-Photon-Number Resolving Detectors and Quantum Computational Advantage

Authors :
Deng, Yu-Hao
Gu, Yi-Chao
Liu, Hua-Liang
Gong, Si-Qiu
Su, Hao
Zhang, Zhi-Jiong
Tang, Hao-Yang
Jia, Meng-Hao
Xu, Jia-Min
Chen, Ming-Cheng
Qin, Jian
Peng, Li-Chao
Yan, Jiarong
Hu, Yi
Huang, Jia
Li, Hao
Li, Yuxuan
Chen, Yaojian
Jiang, Xiao
Gan, Lin
Yang, Guangwen
You, Lixing
Li, Li
Zhong, Han-Sen
Wang, Hui
Liu, Nai-Le
Renema, Jelmer J.
Lu, Chao-Yang
Pan, Jian-Wei
Publication Year :
2023

Abstract

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 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 years using exact methods, whereas our quantum computer, Jiuzhang 3.0, takes only 1.27 us to produce a sample. Generating the hardest sample from the experiment using an exact algorithm would take Frontier ~ 3.1*10^10 years.<br />Comment: PRL 2023 to appear

Subjects

Subjects :
Quantum Physics

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2304.12240
Document Type :
Working Paper