<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Support Vector Machine on Tuan Pham's homepage</title><link>https://tuanmp.github.io/tags/support-vector-machine/</link><description>Recent content in Support Vector Machine on Tuan Pham's homepage</description><generator>Hugo -- 0.128.0</generator><language>en</language><lastBuildDate>Wed, 08 Sep 2021 00:00:00 +0000</lastBuildDate><atom:link href="https://tuanmp.github.io/tags/support-vector-machine/index.xml" rel="self" type="application/rss+xml"/><item><title>Application of quantum machine learning using the quantum kernel algorithm on high energy physics analysis at the LHC</title><link>https://tuanmp.github.io/papers/paper2/</link><pubDate>Wed, 08 Sep 2021 00:00:00 +0000</pubDate><guid>https://tuanmp.github.io/papers/paper2/</guid><description>We studied a support vector machine with a quantum kernel estimator (SQVM-Kernel) for classification of proton-proton final states, targeting the Higgs boson production associated with a pair of top quarks. Using a dataset of 50000 events, we demonstrated the evquivalent performance of the quantum algorithm compared to the classical counterparts.</description></item></channel></rss>