



@article {2451,
	title = {Classifying single-qubit noise using machine learning},
	year = {2019},
	month = {8/30/2019},
	abstract = {<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>},
	url = {https://arxiv.org/abs/1908.11762},
	author = {Travis L. Scholten and Yi-Kai Liu and Kevin Young and Robin Blume-Kohout}
}
