随机电路采样:傅立叶展开和统计

Gil Kalai, Yosef Rinott, Tomer Shoham
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引用次数: 0

摘要

实验量子计算领域的大量工作都集中在有噪声的中间规模量子计算机(NISQ 计算机)上。了解噪声的影响对这项工作的各个方面都很重要,包括实现量子至上的显著目标和演示量子纠错码的尝试。在本文中,我们使用傅立叶方法结合统计分析来研究噪声的影响。特别是,我们使用傅立叶分析来改进线性交叉熵保真度估计器。我们使用分析方法和模拟来研究读出和栅极误差的影响,并使用我们的分析来研究谷歌 2019 年量子纠错实验的样本。
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Random Circuit Sampling: Fourier Expansion and Statistics
Considerable effort in experimental quantum computing is devoted to noisy intermediate scale quantum computers (NISQ computers). Understanding the effect of noise is important for various aspects of this endeavor including notable claims for achieving quantum supremacy and attempts to demonstrate quantum error correcting codes. In this paper we use Fourier methods combined with statistical analysis to study the effect of noise. In particular, we use Fourier analysis to refine the linear cross-entropy fidelity estimator. We use both analytical methods and simulations to study the effect of readout and gate errors, and we use our analysis to study the samples of Google's 2019 quantum supremacy experiment.
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