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Gaussian boson sampling is a promising candidate for showing experimentalquantum advantage. While there is evidence that noiseless Gaussian bosonsampling is hard to efficiently simulate using a classical computer, thecurrent Gaussian boson sampling experiments inevitably suffer from loss andother noise models. Despite a high photon loss rate and the presence of noise,they are currently claimed to be hard to classically simulate with thebest-known classical algorithm. In this work, we present a classicaltensor-network algorithm that simulates Gaussian boson sampling and whosecomplexity can be significantly reduced when the photon loss rate is high. Bygeneralizing the existing thermal-state approximation algorithm of lossyGaussian boson sampling, the proposed algorithm allows us to achieve increasedaccuracy as the running time of the algorithm scales, as opposed to thealgorithm that samples from the thermal state, which can give only a fixedaccuracy. This generalization enables us to simulate the largest scale Gaussianboson sampling experiment so far using relatively modest computationalresources, even though the output state of these experiments is not believed tobe close to a thermal state. By demonstrating that our new classical algorithmoutperforms the large-scale experiments on the benchmarks used as evidence forquantum advantage, we exhibit evidence that our classical sampler can simulatethe ground-truth distribution better than the experiment can, which disputesthe experimental quantum advantage claims.

Classical algorithm for simulating experimental Gaussian boson sampling