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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"/> <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="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; 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. 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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;">2021-05-20. - 777.</td></tr> <!-- End --> <tr><td class="formatRecordLabel"> Series </td><td style="padding-left:5px;">(<a href="/search?f=490__a&p=Conferences">Conferences</a>)<br/>(<a href="/search?f=490__a&p=25th%20International%20Conference%20on%20Computing%20in%20High%20Energy%20%26%20Nuclear%20Physics">25th International Conference on Computing in High Energy & Nuclear Physics</a>)</td></tr> <tr><td class="formatRecordLabel"> Lecture note </td><td style="padding-left:5px;"> on 2021-05-20T11:03:00<br/></td></tr> <tr><td class="formatRecordLabel"> Subject category </td><td style="padding-left:5px;">Conferences</td></tr> <tr><td class="formatRecordLabel"> Abstract </td><td style="padding-left:5px;">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.</td></tr> <tr><td class="formatRecordLabel"> Copyright/License </td><td style="padding-left:5px;"><a href="https://copyright.web.cern.ch/">© 2021-2024 CERN</a></td></tr> <tr><td class="formatRecordLabel"> Submitted by </td><td style="padding-left:5px;"><a href="mailto:graeme.andrew.stewart@cern.ch">graeme.andrew.stewart@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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Meanwhile, it has also been proposed that, in certain circumstances, simulation using GANs can itself be sped-up by using quantum GANs (qGANs).\r\n\r\nOur 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. 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