# Sample caching Markov chain Monte Carlo approach to boson sampling simulation

## TL;DR

This paper introduces a novel sample caching Markov chain Monte Carlo method that reduces sample correlation and loss, enabling more efficient classical simulation of boson sampling, which is crucial for quantum supremacy validation.

## Contribution

The paper proposes a new sample caching MCMC approach that eliminates sample autocorrelation and loss, improving classical boson sampling simulation efficiency and applicability to various sampling tasks.

## Key findings

- Reduces sample autocorrelation in MCMC sampling.
- Prevents sample loss during simulation.
- Enhances efficiency of boson sampling simulation.

## 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.