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Kernel methods are used to enrich the feature representation, allowing better generalization. Quantum kernels implement efficiently complex transformation encoding classical data in the Hilbert space of a quantum system, resulting in even exponential speedup. However, we need prior knowledge of the data to choose an appropriate parametric quantum circuit that can be used as quantum embedding.</p><p>We propose an algorithm that automatically selects the best quantum embedding through a combinatorial optimization procedure that modifies the structure of the circuit, changing the generators of the gates, their angles (which depend on the data points), and the qubits on which the various gates act. Since combinatorial optimization is computationally expensive, we have introduced a criterion based on the exponential concentration of kernel matrix coefficients around the mean to immediately discard an arbitrarily large portion of solutions that are believed to perform poorly.</p><p>Contrary to the gradient-based optimization (e.g. trainable quantum kernels), our approach is not affected by the barren plateau by construction. We have used both artificial and real-world datasets to demonstrate the increased performance of our approach with respect to randomly generated PQC. We have also compared the effect of different optimization algorithms, including greedy local search, simulated annealing, and genetic algorithms, showing that the algorithm choice largely affects the result.</p><p><strong>About the speaker</strong>&nbsp;<br><span style="color:hsl(210,75%,60%);"><strong>Massimiliano Incudin</strong></span>i is PhD student at the Dep. of Computer Science, University of Verona. In his studies, he is focused on the development of quantum kernels for real-world applications.</p><p><strong>Collaborators</strong>&nbsp;<br>The presentation is based on the joint work with Alessandra Di Pierro and Francesco Martini available at&nbsp;<a href="https://arxiv.org/abs/2209.11144">arxiv.org/abs/2209.11144</a>.</p> Incudini, Massimiliano" /> <meta name="keywords" content="CERN Document Server, WebSearch, CERN Document Server" /> <script type="text/javascript" src="https://cds.cern.ch/js/jquery.min.js"></script> <!-- WebNews CSS library --> <link rel="stylesheet" href="https://cds.cern.ch/img/webnews.css" type="text/css" /> <!-- WebNews JS library --> <script type="text/javascript" src="https://cds.cern.ch/js/webnews.js?v=20131009"></script> <meta property="fb:app_id" content="137353533001720"/> <script type="text/x-mathjax-config"> MathJax.Hub.Config({ tex2jax: {inlineMath: [['$','$']], processEscapes: true}, showProcessingMessages: false, messageStyle: "none" }); </script> <script src="/MathJax/MathJax.js?config=TeX-AMS_CHTML" type="text/javascript"> </script> <!-- GoogleScholar --> <meta content="On the construction of useful quantum kernels" name="citation_title" /> <meta content="2023/03/29" name="citation_publication_date" /> <meta name="citation_online_date" content="2023/03/30"> <!-- OpenGraph --> <meta content="On the construction of useful quantum kernels" property="og:title" /> <meta content="website" property="og:type" /> <meta content="website" property="og:type" /> <meta content="https://cds.cern.ch/record/2854770" property="og:url" /> <meta property="og:video:height" content="360" /> <meta property="og:video:width" content="640" /> <meta property="og:video" content="https://lecturemedia.cern.ch/2023/1251853/1251853-presenter-720p-quality.mp4" /> <meta property="og:video:type" content="video/mp4" /> <meta property="og:image" content="https://lecturemedia.cern.ch/2023/1251853/1251853-presenter-cover.jpg" /> <meta name="twitter:player:height" content="360" /> <meta name="twitter:player:width" content="640" /> <link rel="image_src" href="https://lecturemedia.cern.ch/2023/1251853/1251853-presenter-cover.jpg" /> <link rel="video_src" href="https://cds.cern.ch/mediaplayer.swf?file=/1251853/1251853-presenter-720p-quality.mp4&streamer=rtmp://wowza.cern.ch:1935/vod&provider=rtmp&stretching=exactfit&image=https://lecturemedia.cern.ch/2023/1251853/1251853-presenter-cover.jpg"/> <meta name="twitter:player" content="https://cds.cern.ch/video/?"/> <meta content="CERN Document Server" property="og:site_name" /> <meta content="The representation of data is of paramount importance for machine learning methods. Kernel methods are used to enrich the feature representation, allowing better generalization. Quantum kernels implement efficiently complex transformation encoding classical data in the Hilbert space of a quantum system, resulting in even exponential speedup. However, we need prior knowledge of the data to choose an appropriate parametric quantum circuit that can be used as quantum embedding.We propose an algorithm that automatically selects the best quantum embedding through a combinatorial optimization procedure that modifies the structure of the circuit, changing the generators of the gates, their angles (which depend on the data points), and the qubits on which the various gates act. Since combinatorial optimization is computationally expensive, we have introduced a criterion based on the exponential concentration of kernel matrix coefficients around the mean to immediately discard an arbitrarily large portion of solutions that are believed to perform poorly.Contrary to the gradient-based optimization (e.g. trainable quantum kernels), our approach is not affected by the barren plateau by construction. We have used both artificial and real-world datasets to demonstrate the increased performance of our approach with respect to randomly generated PQC. We have also compared the effect of different optimization algorithms, including greedy local search, simulated annealing, and genetic algorithms, showing that the algorithm choice largely affects the result.About the speaker&nbsp;Massimiliano Incudini is PhD student at the Dep. of Computer Science, University of Verona. In his studies, he is focused on the development of quantum kernels for real-world applications.Collaborators&nbsp;The presentation is based on the joint work with Alessandra Di Pierro and Francesco Martini available at&nbsp;arxiv.org/abs/2209.11144." property="og:description" /> <!-- Twitter Card --> <meta content="summary" name="twitter:card" /> <style></style> </head> <body class="CERN32Document32Server search" lang="de"> <!-- toolbar starts --> <div id="cern-toolbar"> <h1><a href="http://cern.ch" title="CERN">CERN <span>Accelerating science</span></a></h1> <ul> <li class="cern-accountlinks"><a class="cern-account" href="https://cds.cern.ch/youraccount/login?ln=de&referer=https%3A//cds.cern.ch/record/2854770%3Fln%3Dde" title="Sign in to your CERN account">Sign in</a></li> <li><a class="cern-directory" href="http://cern.ch/directory" title="Search CERN resources and browse the directory">Directory</a></li> </ul> </div> <!-- toolbar ends --> <!-- Nav header starts--> <div role="banner" class="clearfix" id="header"> <div class="header-inner inner"> <hgroup class="clearfix"> <h2 id="site-name"> <a rel="home" title="Home" href="/"><span>CERN Document Server</span></a> </h2> <h3 id="site-slogan">Access articles, reports and multimedia content in HEP</h3> </hgroup><!-- /#name-and-slogan --> <div role="navigation" id="main-navigation" class="cdsmenu"> <h2 class="element-invisible">Main menu</h2><ul class="links inline clearfix"> <li class="menu-386 first active-trail"><a class="active-trail" href="https://cds.cern.ch/?ln=de">Suchen</a></li> <li class="menu-444 "><a class="" title="" href="https://cds.cern.ch/submit?ln=de">Absenden</a></li> <li class="menu-426 "><a class="" href="https://cds.cern.ch/help/?ln=de">Hilfe</a></li> <li class="leaf hassubcdsmenu"> <a hreflang="en" class="header" href="https://cds.cern.ch/youraccount/display?ln=de">Personalisieren</a> <ul class="subsubcdsmenu"><li><a href="https://cds.cern.ch/youralerts/list?ln=de">Ihre Benachrichtigungen</a></li><li><a href="https://cds.cern.ch/yourbaskets/display?ln=de">Ihre Körbe</a></li><li><a href="https://cds.cern.ch/yourcomments?ln=de">Your comments</a></li><li><a href="https://cds.cern.ch/youralerts/display?ln=de">Ihre Suchanfragen</a></li></ul></li> </ul> </div> </div> </div> <!-- Nav header ends--> <table class="navtrailbox"> <tr> <td class="navtrailboxbody"> <a href="/?ln=de" class="navtrail">Hauptseite</a> > On the construction of useful quantum kernels </td> </tr> </table> </div> <div class="pagebody"><div class="pagebodystripemiddle"> <div class="detailedrecordbox"> <div class="detailedrecordtabs"> <div> <ul class="detailedrecordtabs"><li class="on first"><a href="/record/2854770/?ln=de">Information </a></li><li class="disabled"><a>Dateien </a></li></ul> <div id="tabsSpacer" style="clear:both;height:0px"> </div></div> </div> <div class="detailedrecordboxcontent"> <div class="top-left-folded"></div> <div class="top-right-folded"></div> <div class="inside"> <!--<div style="height:0.1em;"> </div> <p class="notopgap"> </p>--> <abbr class="unapi-id" title="2854770"></abbr> <!-- Add download buttons css --> <link href="/img/download_and_embed_buttons.css" rel="stylesheet" type="text/css" /> <style type="text/css"> <!-- ul.detailedrecordtabs li.on a{background-color:#4D94CC;color:#fff !important;border-bottom:1px solid #4D94CC!important;} div.detailedrecordboxcontent {padding-top:0px !important;} table.formatRecordTableFullWidth #download_movie_box { width: 500px; 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Geneva</td></tr> <!-- In case of ``980__a:E-LEARNING``, the label is changed by a javascript code above as requested on RQF0820114 --> <script type="text/javascript"> /* * Changes the Imprint title to Date - Duration as requested by RQF0820114 * This is a hack not to touch the general imprint format element */ $(document).ready(function() { var collections = 'Indico'; if (collections.indexOf('E-LEARNING') > -1) { $('.cern-imprint-label').text('Date - Duration'); } }) </script> <tr><td class="formatRecordLabel cern-imprint-label"> Imprint </td><td style="padding-left:5px;">2023-03-29. - 4512.</td></tr> <!-- End --> <tr><td class="formatRecordLabel"> Series </td><td style="padding-left:5px;">(<a href="/search?f=490__a&p=QTI%20Lectures">QTI Lectures</a>)</td></tr> <tr><td class="formatRecordLabel"> Lecture note </td><td style="padding-left:5px;"> on 2023-03-29T14:30:00<br/></td></tr> <tr><td class="formatRecordLabel"> Subject category </td><td style="padding-left:5px;">QTI Lectures</td></tr> <tr><td class="formatRecordLabel"> Abstract </td><td style="padding-left:5px;"><p>The representation of data is of paramount importance for machine learning methods. Kernel methods are used to enrich the feature representation, allowing better generalization. Quantum kernels implement efficiently complex transformation encoding classical data in the Hilbert space of a quantum system, resulting in even exponential speedup. However, we need prior knowledge of the data to choose an appropriate parametric quantum circuit that can be used as quantum embedding.</p><p>We propose an algorithm that automatically selects the best quantum embedding through a combinatorial optimization procedure that modifies the structure of the circuit, changing the generators of the gates, their angles (which depend on the data points), and the qubits on which the various gates act. Since combinatorial optimization is computationally expensive, we have introduced a criterion based on the exponential concentration of kernel matrix coefficients around the mean to immediately discard an arbitrarily large portion of solutions that are believed to perform poorly.</p><p>Contrary to the gradient-based optimization (e.g. trainable quantum kernels), our approach is not affected by the barren plateau by construction. We have used both artificial and real-world datasets to demonstrate the increased performance of our approach with respect to randomly generated PQC. We have also compared the effect of different optimization algorithms, including greedy local search, simulated annealing, and genetic algorithms, showing that the algorithm choice largely affects the result.</p><p><strong>About the speaker</strong> <br><span style="color:hsl(210,75%,60%);"><strong>Massimiliano Incudin</strong></span>i is PhD student at the Dep. of Computer Science, University of Verona. In his studies, he is focused on the development of quantum kernels for real-world applications.</p><p><strong>Collaborators</strong> <br>The presentation is based on the joint work with Alessandra Di Pierro and Francesco Martini available at <a href="https://arxiv.org/abs/2209.11144">arxiv.org/abs/2209.11144</a>.</p></td></tr> <tr><td class="formatRecordLabel"> Copyright/License </td><td style="padding-left:5px;"><a href="https://copyright.web.cern.ch/">© 2023-2024 CERN</a></td></tr> <tr><td class="formatRecordLabel"> Submitted by </td><td style="padding-left:5px;"><a href="mailto:miguel.marquina@cern.ch">miguel.marquina@cern.ch</a></td></tr> </table> <br/><div><div style="clear: both;"> </div></div> <script type="text/javascript"> // Initially hide: $(".longCaption").hide(); // Allow to toggle visibility: $(".toggleLongCaption").toggle(function(){ $(this).siblings(".longCaption").show('fast'); var thisElem = $(this); thisElem.text(thisElem.text() === "more" ? 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Kernel methods are used to enrich the feature representation, allowing better generalization. Quantum kernels implement efficiently complex transformation encoding classical data in the Hilbert space of a quantum system, resulting in even exponential speedup. However, we need prior knowledge of the data to choose an appropriate parametric quantum circuit that can be used as quantum embedding.</p><p>We propose an algorithm that automatically selects the best quantum embedding through a combinatorial optimization procedure that modifies the structure of the circuit, changing the generators of the gates, their angles (which depend on the data points), and the qubits on which the various gates act. Since combinatorial optimization is computationally expensive, we have introduced a criterion based on the exponential concentration of kernel matrix coefficients around the mean to immediately discard an arbitrarily large portion of solutions that are believed to perform poorly.</p><p>Contrary to the gradient-based optimization (e.g. trainable quantum kernels), our approach is not affected by the barren plateau by construction. We have used both artificial and real-world datasets to demonstrate the increased performance of our approach with respect to randomly generated PQC. We have also compared the effect of different optimization algorithms, including greedy local search, simulated annealing, and genetic algorithms, showing that the algorithm choice largely affects the result.</p><p><strong>About the speaker</strong> <br><span style=\"color:hsl(210,75%,60%);\"><strong>Massimiliano Incudin</strong></span>i is PhD student at the Dep. of Computer Science, University of Verona. In his studies, he is focused on the development of quantum kernels for real-world applications.</p><p><strong>Collaborators</strong> <br>The presentation is based on the joint work with Alessandra Di Pierro and Francesco Martini available at <a href=\"https://arxiv.org/abs/2209.11144\">arxiv.org/abs/2209.11144</a>.</p>" }); // ]]> </script> <!-- JQuery Bookmark Button END --> </div> </div> <div style="clear:both;margin-bottom: 0;"></div> </div> <div class="bottom-left"></div><div class="bottom-right"></div> </div> </div></div> <footer id="footer" class="pagefooter clearfix"> <!-- replaced page footer --> <div class="pagefooterstripeleft"> CERN Document Server :: <a class="footer" href="https://cds.cern.ch/?ln=de">Suchen</a> :: <a class="footer" href="https://cds.cern.ch/submit?ln=de">Absenden</a> :: <a class="footer" href="https://cds.cern.ch/youraccount/display?ln=de">Personalisieren</a> :: <a class="footer" href="https://cds.cern.ch/help/?ln=de">Hilfe</a> :: <a class="footer" href="https://cern.service-now.com/service-portal?id=privacy_policy&se=CDS-Service" target="_blank">Privacy Notice</a> <br /> Powered by <a class="footer" href="http://invenio-software.org/">Invenio</a> <br /> Verwaltet von <a class="footer" href="https://cern.service-now.com/service-portal?id=service_element&name=CDS-Service">CDS Service</a> - Need help? 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