Extension of a pattern recognition validation approach for noisy boson sampling

The article delves into the extension of pattern recognition validation techniques to evaluate the impact of noises, specifically photon distinguishability and loss, on boson sampling. It begins by discussing the potential of boson sampling in challenging the extended Church-Turing thesis and its applications in drug design and graph problem-solving. The study highlights the computational complexity and the reliability issues of traditional validation methods, such as the Bayesian validation, when dealing with partially indistinguishable photons. The extended pattern recognition validation approach is introduced as a more suitable method for large-scale boson sampling, leveraging the unique data structure of boson sampling outputs. The article explores the intrinsic data structures of boson sampling, revealing how the probability distribution and norm distances of output events change with photon indistinguishability. It also examines the effects of photon loss on validation performances and presents examples of larger-scale boson sampling validations. The findings suggest that the extended validation methods can effectively distinguish quantum-like behaviors from classical ones, even in the presence of significant noises, paving the way for future experimental validations of boson sampling.