1. Sample caching Markov chain Monte Carlo approach to boson sampling simulation
- Author
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Yong Liu, Min Xiong, Chunqing Wu, Dongyang Wang, Yingwen Liu, Jiangfang Ding, Anqi Huang, Xiang Fu, Xiaogang Qiang, Ping Xu, Mingtang Deng, Xuejun Yang, and Junjie Wu
- Subjects
boson sampling ,quantum supremacy ,Markov chain Monte Carlo ,autocorrelation ,sample caching ,Science ,Physics ,QC1-999 - Abstract
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.
- Published
- 2020
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