GBS量子计算机的验证测试为具有退相干目标的量子优势提供了证据
Validation tests of GBS quantum computers give evidence for quantum advantage with a decoherent target
AI总结：
本文提出精确可扩展的高阶统计测试验证大规模GBS量子计算机，利用正P相空间模拟分组计数概率作为指纹，与144通道实验对比发现数据更符合部分热化量子模型，为退相干目标问题的量子优势提供证据。
摘要：
计算验证对所有大规模量子计算机至关重要。人们需要既快速又准确的计算机。在此，我们将精确、可扩展的高阶统计测试应用于声称具有量子计算优势的大规模高斯玻色采样（GBS）量子计算机的数据。这些测试可用于验证此类技术的输出结果。我们的方法允许对准确性以及量子优势进行研究。这些问题以前尚未得到详细研究。我们高度可扩展的技术也适用于线性玻色网络的其他应用。我们利用分组计数概率（GCP）的正P相空间模拟作为验证多模数据的指纹。由于采样误差低得多，这比其他相空间方法呈指数级更高效。我们从指数级多的高阶分组计数测试中随机生成测试。每个测试都可以高效地测量和模拟，提供了一种难以通过经典方式复制的量子验证方法。我们对理论与144通道GBS实验进行了详细比较，包括高达所测最大阶数的分组相关性。我们展示了如何证伪伪造数据，并将其应用于经典计数算法。评估分布的保真度和计算复杂度有多种距离度量。我们计算并解释了这些度量。与数据最拟合的是部分热化的高斯模型，它既不是理想情况，也不是给出经典可计算计数的模型。即使使用该模型，在一些χ²测试中也观察到了Z>100的差异，表明可能存在参数估计误差。总计数分布比经典模型更接近热化量子模型，提供了与改进目标问题的量子计算优势相一致的证据。
英文摘要：
Computational validation is vital for all large-scale quantum computers. One needs computers that are both fast and accurate. Here we apply precise, scalable, high order statistical tests to data from large Gaussian boson sampling (GBS) quantum computers that claim quantum computational advantage. These tests can be used to validate the output results for such technologies. Our method allows investigation of accuracy as well as quantum advantage. Such issues have not been investigated in detail before. Our highly scalable technique is also applicable to other applications of linear bosonic networks. We utilize positive-P phase-space simulations of grouped count probabilities (GCP) as a fingerprint for verifying multi-mode data. This is exponentially more efficient than other phase-space methods, due to much lower sampling errors. We randomly generate tests from exponentially many high-order, grouped count tests. Each of these can be efficiently measured and simulated, providing a quantum verification method that is hard to replicate classically. We give a detailed comparison of theory with a 144-channel GBS experiment, including grouped correlations up to the largest order measured. We show how one can disprove faked data, and apply this to a classical count algorithm. There are multiple distance measures for evaluating the fidelity and computational complexity of a distribution. We compute these and explain them. The best fit to the data is a partly thermalized Gaussian model, which is neither the ideal case, nor the model that gives classically computable counts. Even with this model, discrepancies of $Z>100$ were observed from some $χ^2$ tests, indicating likely parameter estimation errors. Total count distributions were much closer to a thermalized quantum model than the classical model, giving evidence consistent with quantum computational advantage for a modified target problem.
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