  Gaussian boson sampling is a promising candidate for showing experimental
quantum advantage. While there is evidence that noiseless Gaussian boson
sampling is hard to efficiently simulate using a classical computer, the
current Gaussian boson sampling experiments inevitably suffer from loss and
other noise models. Despite a high photon loss rate and the presence of noise,
they are currently claimed to be hard to classically simulate with the
best-known classical algorithm. In this work, we present a classical
tensor-network algorithm that simulates Gaussian boson sampling and whose
complexity can be significantly reduced when the photon loss rate is high. By
generalizing the existing thermal-state approximation algorithm of lossy
Gaussian boson sampling, the proposed algorithm allows us to achieve increased
accuracy as the running time of the algorithm scales, as opposed to the
algorithm that samples from the thermal state, which can give only a fixed
accuracy. This generalization enables us to simulate the largest scale Gaussian
boson sampling experiment so far using relatively modest computational
resources, even though the output state of these experiments is not believed to
be close to a thermal state. By demonstrating that our new classical algorithm
outperforms the large-scale experiments on the benchmarks used as evidence for
quantum advantage, we exhibit evidence that our classical sampler can simulate
the ground-truth distribution better than the experiment can, which disputes
the experimental quantum advantage claims.
