On classical simulation algorithms for noisy Boson Sampling

• URL: http://arxiv.org/abs/2301.11532v1
• Date: Fri, 27 Jan 2023 04:42:59 GMT
• Title: On classical simulation algorithms for noisy Boson Sampling
• Authors: Changhun Oh, Liang Jiang, Bill Fefferman
• Abstract summary: We present a classical algorithm that approximately samples from the output distribution of certain noisy Boson Sampling experiments. This is inspired by a recent result of Aharonov, Gao, Landau, Liu and Vazirani and makes use of an observation originally due to Kalai Kindler.
• Score: 2.8655318786364408
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a classical algorithm that approximately samples from the output distribution of certain noisy Boson Sampling experiments. This algorithm is inspired by a recent result of Aharonov, Gao, Landau, Liu and Vazirani and makes use of an observation originally due to Kalai and Kindler that the output probability of Boson Sampling experiments with a Gaussian noise model can be approximated by sparse low-degree polynomials. This observation alone does not suffice for classical sampling, because its marginal probabilities might not be approximated by sparse low-degree polynomials, and furthermore, the approximated probabilities might be negative. We solve this problem by employing the first quantization representation to give an algorithm for computing the marginal probabilities of these experiments. We prove that when the overall noise rate is constant, the algorithm runs in time quasi-polynomial in the number of input photons $N$ and accuracy. When the overall noise rate scales as $1-x_1^\gamma$ for constant $x_1$ and $\gamma=\Omega(\log N)$, the running time becomes polynomial. Furthermore, we study noisy Boson Sampling with practically relevant noise models such as partial distinguishability and photon loss. We show that the same technique does not immediately apply in these settings, leaving open the possibility of a scalable demonstration of noisy quantum advantage for these noise models in certain parameter regimes.

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