



01696nas a2200145   4500008004100000245005800041210005700099260001400156520126100170100002501431700001601456700001701472700002401489856003701513       2019                        eng d00aClassifying single-qubit noise using machine learning0 aClassifying singlequbit noise using machine learning  c8/30/20193 a<p>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 &quot;machine-learned&quot; 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 &quot;natural&quot; 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.&nbsp;</p>1 aScholten, Travis, L.1 aLiu, Yi-Kai1 aYoung, Kevin1 aBlume-Kohout, Robin  uhttps://arxiv.org/abs/1908.11762