arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1908.11762quant-phcs.LG
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使用机器学习分类单量子比特噪声

Classifying single-qubit noise using machine learning

IBM T.J. Watson Research Center(IBM T.J.沃森研究中心);National Institute of Standards and Technology(美国国家标准与技术研究院);University of Maryland(马里兰大学);Sandia National Laboratories(桑迪亚国家实验室);University of New Mexico(新墨西哥大学)

Travis L. Scholten, Yi-Kai Liu, Kevin Young, Robin Blume-Kohout

摘要:

量子表征、确认和验证(QCVV)技术用于探测、表征、诊断和检测量子信息处理器(QIP)中的错误。任何QCVV协议的一个重要组成部分是从实验数据到QIP属性估计的映射。机器学习(ML)算法可以帮助自动化QCVV协议的开发,通过从训练数据中学习来创建此类地图。我们确定了“机器学习”的QCVV技术的关键组成部分,并提出了一个标题为开发它们。为了证明这种方法,我们专注于确定影响单个量子位的噪声是相干的还是随机的(不相干的),使用最初提出的数据集进行门集层析成像的问题。我们利用已知的ML算法来训练区分这两种噪声的分类器。分类器的准确性取决于它能多好地近似训练数据的“自然”几何形状。我们发现由噪声量子比特生成的GST数据集可以通过线性表面可靠地分离,尽管特征工程可能是必要的。我们还表明,通过支持向量机(SVM)学习的分类器在有限样本噪声下是鲁棒的。

英文摘要:

Quantum characterization, validation, and verification (QCVV) techniques are used to probe, characterize, diagnose, and detect errors in quantum information processors (QIPs). An important component of any QCVV protocol is a mapping from experimental data to an estimate of a property of a QIP. Machine learning (ML) algorithms can help automate the development of QCVV protocols, creating such maps by learning them from training data. We identify the critical components of "machine-learned" QCVV techniques, and present a rubric for developing them. To demonstrate this approach, we focus on the problem of determining whether noise affecting a single qubit is coherent or stochastic (incoherent) using the data sets originally proposed for gate set tomography. We leverage known ML algorithms to train a classifier distinguishing these two kinds of noise. The accuracy of the classifier depends on how well it can approximate the "natural" geometry of the training data. We find GST data sets generated by a noisy qubit can reliably be separated by linear surfaces, although feature engineering can be necessary. We also show the classifier learned by a support vector machine (SVM) is robust under finite-sample noise.

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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