近日，美国芝加哥大学的Changhun Oh及其研究团队取得一项新进展。经过不懈努力，他们提出模拟实验高斯玻色子采样的经典算法。相关研究成果已于2024年6月25日在国际知名学术期刊《自然—物理学》上发表。

本文提出了一种模拟高斯玻色子采样的经典张量网络算法，当面临较高的光子损失率时，该算法能够显著降低计算复杂度。该算法使得研究人员能够利用相对有限的计算资源，模拟迄今为止最大规模的高斯玻色子采样实验。本研究提供的证据表明，该经典采样器在模拟理想分布方面表现优于实验，这对实验量子优势的主张提出了有力质疑。

据悉，高斯玻色子采样是一种非通用量子计算形式，被认为是展示实验量子优势的有希望的候选者。虽然有证据表明高斯玻色子的无噪声采样很难用经典计算机有效地模拟，然而，目前的高斯玻色子采样实验不可避免地受到高光子损失率以及其他噪声源的干扰，尽管如此，这些实验仍然被认为是难以通过经典计算机进行模拟的。

附：英文原文

Title: Classical algorithm for simulating experimental Gaussian boson sampling

Author: Oh, Changhun, Liu, Minzhao, Alexeev, Yuri, Fefferman, Bill, Jiang, Liang

Issue&Volume: 2024-06-25

Abstract: 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.

DOI: 10.1038/s41567-024-02535-8

Source: https://www.nature.com/articles/s41567-024-02535-8