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Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics - CERN Document Server

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Meanwhile, it has also been proposed that, in certain circumstances, simulation using GANs can itself be sped-up by using quantum GANs (qGANs). Our work presents an advanced prototype of qGAN, that we call the dual-Parameterized Quantum Circuit (PQC) GAN, with a classical discriminator and two quantum generators which take the form of PQCs. The first PQC learns the probability distribution over the images of $N$ pixels, while the second generates normalized pixel intensities of an individual image for each PQC input. The performance of the dual-PQC architecture has been evaluated through the application in HEP to imitate calorimeter outputs, translated into pixelated images. The results demonstrate that the model can reproduce a fixed number of images with a reduced size as well as their probability distribution and we anticipate it should allow us to scale up to real calorimeter outputs. Chang, Su Yeon" /> <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"/> <!-- GoogleScholar --> <meta content="Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics" name="citation_title" /> <meta content="2021/05/20" name="citation_publication_date" /> <meta name="citation_online_date" content="2021/05/21"> <!-- OpenGraph --> <meta content="25th International Conference on Computing in High Energy &amp;amp; Nuclear Physics" property="og:title" /> <meta content="Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics" property="og:title" /> <meta content="website" property="og:type" /> <meta content="website" property="og:type" /> <meta content="https://cds.cern.ch/record/2767275" 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/2021/948465c157/948465c157-1000-kbps-853x480-25-fps-audio-96-kbps-44-kHz-stereo.mp4" /> <meta property="og:video:type" content="video/mp4" /> <meta property="og:image" content="https://lecturemedia.cern.ch/2021/948465c157/948465c157-posterframe-640x360-at-5.0-percent.jpg" /> <meta name="twitter:player:height" content="360" /> <meta name="twitter:player:width" content="640" /> <link rel="image_src" href="https://lecturemedia.cern.ch/2021/948465c157/948465c157-posterframe-640x360-at-5.0-percent.jpg" /> <link rel="video_src" href="https://cds.cern.ch/mediaplayer.swf?file=/948465c157/948465c157-1000-kbps-853x480-25-fps-audio-96-kbps-44-kHz-stereo.mp4&streamer=rtmp://wowza.cern.ch:1935/vod&provider=rtmp&stretching=exactfit&image=https://lecturemedia.cern.ch/2021/948465c157/948465c157-posterframe-640x360-at-5.0-percent.jpg"/> <meta name="twitter:player" content="https://cds.cern.ch/video/?"/> <meta content="CERN Document Server" property="og:site_name" /> <meta content="Generative Models, and Generative Adversarial Networks (GAN) in particular, are being studied as possible alternatives to Monte Carlo. Meanwhile, it has also been proposed that, in certain circumstances, simulation using GANs can itself be sped-up by using quantum GANs (qGANs). Our work presents an advanced prototype of qGAN, that we call the dual-Parameterized Quantum Circuit (PQC) GAN, with a classical discriminator and two quantum generators which take the form of PQCs. The first PQC learns the probability distribution over the images of $N$ pixels, while the second generates normalized pixel intensities of an individual image for each PQC input. The performance of the dual-PQC architecture has been evaluated through the application in HEP to imitate calorimeter outputs, translated into pixelated images. The results demonstrate that the model can reproduce a fixed number of images with a reduced size as well as their probability distribution and we anticipate it should allow us to scale up to real calorimeter outputs." property="og:description" /> <!-- Twitter Card --> <meta content="summary" name="twitter:card" /> <style></style> </head> <body class="CERN32Document32Server search" lang="en"> <!-- 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=en&amp;referer=https%3A//cds.cern.ch/record/2767275/export/hx%3Fln%3Den" 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=en">Search</a></li> <li class="menu-444 "><a class="" title="" href="https://cds.cern.ch/submit?ln=en">Submit</a></li> <li class="menu-426 "><a class="" href="https://cds.cern.ch/help/?ln=en">Help</a></li> <li class="leaf hassubcdsmenu"> <a hreflang="en" class="header" href="https://cds.cern.ch/youraccount/display?ln=en">Personalize</a> <ul class="subsubcdsmenu"><li><a href="https://cds.cern.ch/youralerts/list?ln=en">Your alerts</a></li><li><a href="https://cds.cern.ch/yourbaskets/display?ln=en">Your baskets</a></li><li><a href="https://cds.cern.ch/yourcomments?ln=en">Your comments</a></li><li><a href="https://cds.cern.ch/youralerts/display?ln=en">Your searches</a></li></ul></li> </ul> </div> </div> </div> <!-- Nav header ends--> <table class="navtrailbox"> <tr> <td class="navtrailboxbody"> <a href="/?ln=en" class="navtrail">Home</a> &gt; <a class="navtrail" href="/record/2767275">Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics</a> &gt; BibTeX </td> </tr> </table> </div> <div class="pagebody"><div class="pagebodystripemiddle"> <pre> @article{Chang:2767275, author = "Chang, Su Yeon", title = "{Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics. 25th International Conference on Computing in High Energy &amp;amp; Nuclear Physics}", year = "2021", url = "https://cds.cern.ch/record/2767275", } </pre></div></div> <footer id="footer" class="pagefooter clearfix"> <!-- replaced page footer --> <div class="pagefooterstripeleft"> CERN Document Server&nbsp;::&nbsp;<a class="footer" href="https://cds.cern.ch/?ln=en">Search</a>&nbsp;::&nbsp;<a class="footer" href="https://cds.cern.ch/submit?ln=en">Submit</a>&nbsp;::&nbsp;<a class="footer" href="https://cds.cern.ch/youraccount/display?ln=en">Personalize</a>&nbsp;::&nbsp;<a class="footer" href="https://cds.cern.ch/help/?ln=en">Help</a>&nbsp;::&nbsp;<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 /> Maintained by <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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