We investigate the use of machine learning (ML) algorithms for developing new QCVV protocols. ML algorithms learn approximations to functions that relate experimental data to some property of interest. As an example, we show ML algorithms can successfully learn separating surfaces for distinguishing coherent and stochastic noise affecting a single qubit. The performance of various ML algorithms depends strongly on the geometry of experimental data (in this case, data from gate set tomography experiments). We show performance can be boosted by hyperparameter tuning and feature engineering.Sandia National Labs is managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a subsidiary of Honeywell International, Inc., for the U.S. Dept. of Energy's National Nuclear Security Administration under contract DE-NA0003525. The views expressed in this abstract do not necessarily represent the views of the DOE or the U.S. Government. Contributions to this work by NIST, an agency of the US government, are not subject to US copyright. Any mention of commercial products is for informational purposes only, and does not indicate endorsement by NIST. .
APS March Meeting Abstracts
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