Sample caching Markov chain Monte Carlo approach to boson sampling simulation

Boson sampling is a promising candidate for quantum supremacy. It requires to sample from a complicated distribution, and is trusted to be intractable on classical computers. Among the various classical sampling methods, the Markov chain Monte Carlo method is an important approach to the simulation...

Full description

Saved in:
Bibliographic Details
Published inNew journal of physics Vol. 22; no. 3; pp. 33022 - 33029
Main Authors Liu, Yong, Xiong, Min, Wu, Chunqing, Wang, Dongyang, Liu, Yingwen, Ding, Jiangfang, Huang, Anqi, Fu, Xiang, Qiang, Xiaogang, Xu, Ping, Deng, Mingtang, Yang, Xuejun, Wu, Junjie
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.03.2020
Subjects
autocorrelation
Bayesian analysis
boson sampling
Caching
Computer simulation
Markov analysis
Markov chain Monte Carlo
Markov chains
Monte Carlo simulation
Physics
quantum supremacy
sample caching
Sampling methods
Online AccessGet full text

Cover

More Information
Summary:Boson sampling is a promising candidate for quantum supremacy. It requires to sample from a complicated distribution, and is trusted to be intractable on classical computers. Among the various classical sampling methods, the Markov chain Monte Carlo method is an important approach to the simulation and validation of boson sampling. This method however suffers from the severe sample loss issue caused by the autocorrelation of the sample sequence. Addressing this, we propose the sample caching Markov chain Monte Carlo method that eliminates the correlations among the samples, and prevents the sample loss at the meantime, allowing more efficient simulation of boson sampling. Moreover, our method can be used as a general sampling framework that can benefit a wide range of sampling tasks, and is particularly suitable for applications where a large number of samples are taken.
Bibliography:NJP-110883.R2
ISSN:1367-2630
1367-2630
DOI:10.1088/1367-2630/ab73c4