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arXiv 2303.00780quant-ph
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在39比特量子处理器中学习相关噪声

Learning correlated noise in a 39-qubit quantum processor

Robin Harper, Steven T. Flammia

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11+11 pages, 10 figures. Comments welcome
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AI总结：

本文提出一种提取量子器件检错子提取线路中噪声信息的方法，在39比特超导器件上构建图模型精确学习相关噪声，并证明准确的关联噪声模型对预测量子纠错亚阈值行为至关重要。

摘要：

构建容错量子计算机关键依赖于对候选器件中噪声的测量与建模。特别是，最优纠错需要了解器件在执行纠错所需线路时发生的噪声。随着器件规模增大，我们将更加依赖此类噪声的高效模型。然而，这些模型仍必须保留用于优化纠错算法所需的信息。在此，我们提出了一种提取运行检错子提取线路的器件中噪声详细信息的方法。我们在一台超导器件上引入并执行了一项实验，使用其39个量子比特构成表面码进行多轮检错子提取，但省略了线路中途的测量与重置。我们展示了如何从20个数据量子比特中提取所需信息，以图模型的形式构建不同复杂度的噪声模型。这些模型给出了大规模器件中噪声的高效描述，旨在阐明纠错对抗相关噪声的有效性。此外，我们的估计十分精确：我们学习到了一致的全局分布，其中所有单比特和双比特错误率的相对误差均已知为0.1%。通过将实验学习到的噪声模型外推至更低错误率，我们证明了准确的关联噪声模型对于成功预测量子纠错实验中的亚阈值行为越来越重要。

英文摘要：

Building error-corrected quantum computers relies crucially on measuring and modeling noise on candidate devices. In particular, optimal error correction requires knowing the noise that occurs in the device as it executes the circuits required for error correction. As devices increase in size we will become more reliant on efficient models of this noise. However, such models must still retain the information required to optimize the algorithms used for error correction. Here we propose a method of extracting detailed information of the noise in a device running syndrome extraction circuits. We introduce and execute an experiment on a superconducting device using 39 of its qubits in a surface code doing repeated rounds of syndrome extraction, but omitting the mid-circuit measurement and reset. We show how to extract from the 20 data qubits the information needed to build noise models of various sophistication in the form of graphical models. These models give efficient descriptions of noise in large-scale devices and are designed to illuminate the effectiveness of error correction against correlated noise. Our estimates are furthermore precise: we learn a consistent global distribution where all one- and two-qubit error rates are known to a relative error of 0.1%. By extrapolating our experimentally learned noise models towards lower error rates, we demonstrate that accurate correlated noise models are increasingly important for successfully predicting sub-threshold behavior in quantum error correction experiments.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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