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. 2021 Nov 9;11:21905. doi: 10.1038/s41598-021-01445-6

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© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2021

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Scalability of Scikit-learn regression (blue bars and dotted line) and D-Wave regression (yellow and red bars, and bold line). X-axis shows number of features in the training set ( $d + 1$ ), ranging from 2 to 32. The Y-axis shows run time in milliseconds on a logarithmic scale. We observe a $2.8 \times$ speedup using D-Wave when $(d + 1)$ equals 32.