Classical algorithm for simulating experimental Gaussian boson sampling
2024-06-25 09:13
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Gaussian boson sampling is a form of non-universal quantum computing
that has been considered a promising candidate for showing experimental
quantum advantage. While there is evidence that noiseless Gaussian
boson sampling is hard to efciently simulate using a classical computer,
current Gaussian boson sampling experiments inevitably sufer from high
photon loss rates and other noise sources. Nevertheless, they are currently
claimed to be hard to classically simulate. Here we present a classical
tensor-network algorithm that simulates Gaussian boson sampling and
whose complexity can be signifcantly reduced when the photon loss rate
is high. Our algorithm enables us to simulate the largest-scale Gaussian
boson sampling experiment so far using relatively modest computational
resources. We exhibit evidence that our classical sampler can simulate the
ideal distribution better than the experiment can, which calls into question
the claims of experimental quantum advantage.
Article:https://www.nature.com/articles/s41567-024-02535-8
