Classical algorithm for simulating experimental Gaussian boson sampling

doi:10.1038/s41567-024-02535-8

Classical algorithm for simulating experimental Gaussian boson sampling

2024-06-25

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DOI：10.1038/s41567-024-02535-8

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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 efficiently simulate using a classical computer, current Gaussian boson sampling experiments inevitably suffer 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 significantly 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. Gaussian boson sampling reproduces distributions that are hard to calculate classically and were claimed to show quantum advantage in the noiseless limit. But now a classical algorithm is shown to reproduce experimental results when noise is large.

Keywords:

QUANTUM COMPUTATIONAL ADVANTAGE

COMPLEXITY

SUPREMACY

Classical algorithm for simulating experimental Gaussian boson sampling

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