{"id":4172,"date":"2020-12-07T18:00:00","date_gmt":"2020-12-07T18:00:00","guid":{"rendered":"https:\/\/www.aiproblog.com\/index.php\/2020\/12\/07\/researchers-can-use-qsim-to-explore-quantum-algorithms\/"},"modified":"2020-12-07T18:00:00","modified_gmt":"2020-12-07T18:00:00","slug":"researchers-can-use-qsim-to-explore-quantum-algorithms","status":"publish","type":"post","link":"https:\/\/www.aiproblog.com\/index.php\/2020\/12\/07\/researchers-can-use-qsim-to-explore-quantum-algorithms\/","title":{"rendered":"Researchers can use qsim to explore quantum algorithms"},"content":{"rendered":"<p>Author: <\/p>\n<div>\n<div class=\"block-paragraph\">\n<div class=\"rich-text\">\n<p>A year ago, Google&rsquo;s Quantum AI team achieved a <a href=\"https:\/\/blog.google\/perspectives\/sundar-pichai\/what-our-quantum-computing-milestone-means\/\">beyond-classical computation<\/a> by using a quantum computer to outperform the world&rsquo;s fastest classical computer. With this, we entered a new era of quantum computing. We still have a long journey ahead of us to find practical applications, and we know we can&rsquo;t get there alone. So today we&rsquo;re launching <a href=\"https:\/\/quantumai.google\/qsim\">qsim<\/a>, a new open source quantum simulator that will help researchers develop quantum algorithms.&nbsp;<\/p>\n<p><\/p>\n<h3>The importance of simulators in quantum computing<\/h3>\n<p>Simulators are important tools for writing and debugging quantum code, and they&rsquo;re essential for developing quantum algorithms. The few experimental quantum processors currently available, like the <a href=\"https:\/\/ai.googleblog.com\/2019\/10\/quantum-supremacy-using-programmable.html\">one that achieved a beyond-classical computation<\/a>, are prone to noise and don&rsquo;t perform error correction. This is where simulators like qsim come in. They allow researchers to explore quantum algorithms under idealized conditions and are more readily available. They also help prepare experiments to run on actual quantum hardware.<\/p>\n<\/p>\n<p><a href=\"https:\/\/quantumai.google\/qsim\">qsim<\/a> can simulate around 30 qubits on a laptop, or up to 40 qubits in <a href=\"https:\/\/cloud.google.com\/compute\/docs\/machine-types\">Google Cloud<\/a>. What used to take an expensive cluster of computers to simulate can now be done on a single computer with qsim. We use qsim frequently at Google to test and benchmark quantum algorithms and processors. One example of this is our research in quantum neural networks. By using qsim with <a href=\"https:\/\/quantumai.google\/cirq\">Cirq<\/a> and <a href=\"https:\/\/www.tensorflow.org\/quantum\">TensorFlow Quantum<\/a>, we&rsquo;ve trained <a href=\"https:\/\/blog.tensorflow.org\/2020\/11\/characterizing-quantum-advantage-in.html\">quantum ML models<\/a> involving hundreds of thousands of circuits.&nbsp;<\/p>\n<p><\/p>\n<h3>Open source software tools for developing quantum algorithms<\/h3>\n<p>qsim is part of our <a href=\"https:\/\/quantumai.google\/software\">open source ecosystem of software tools<\/a>. These include Cirq, our quantum programming framework, <a href=\"https:\/\/quantumai.google\/cirq\/experiments\">ReCirq<\/a>, a repository of research examples, and application-specific libraries such as <a href=\"https:\/\/quantumai.google\/openfermion\">OpenFermion<\/a> for quantum chemistry and <a href=\"https:\/\/www.tensorflow.org\/quantum\">TensorFlow Quantum<\/a> for quantum machine learning. These tools are designed to work together and to help you get started easily. Researchers who have developed quantum algorithms with Cirq can now use qsim by changing one line of code in <a href=\"https:\/\/quantumai.google\/qsim\/tutorials\/qsimcirq\">Colab<\/a> and experience an instant speedup in their circuit simulations. <\/p>\n<\/div>\n<\/div>\n<div class=\"block-image_full_width\">\n<div class=\"article-module h-c-page\">\n<div class=\"h-c-grid\">\n<figure class=\"article-image--large h-c-grid__col h-c-grid__col--6 h-c-grid__col--offset-3 \"><img decoding=\"async\" alt=\"Google Quantum AI website\" src=\"https:\/\/storage.googleapis.com\/gweb-uniblog-publish-prod\/images\/qsim_blog_post_asset.max-1000x1000.png\"><\/figure>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"block-paragraph\">\n<div class=\"rich-text\">\n<p>To help you get started with qsim and our other open source quantum software, we&rsquo;ve launched a <a href=\"https:\/\/quantumai.google\/\">new website<\/a> that brings together all of our tools, research initiatives, and educational material. Researchers can access our <a href=\"https:\/\/quantumai.google\/research\">latest publications and research repositories<\/a>, students can find <a href=\"https:\/\/quantumai.google\/education\">educational resources<\/a> or apply for <a href=\"https:\/\/quantumai.google\/team\/careers\">internships<\/a>, and developers interested in quantum computing can <a href=\"https:\/\/quantumai.google\/#community\">join our growing community of contributors<\/a>.&nbsp;<\/p>\n<\/div>\n<\/div>\n<\/div>\n<p><a href=\"https:\/\/blog.google\/technology\/ai\/qsim-explore-quantum-algorithms\/\">Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Author: A year ago, Google&rsquo;s Quantum AI team achieved a beyond-classical computation by using a quantum computer to outperform the world&rsquo;s fastest classical computer. With [&hellip;] <span class=\"read-more-link\"><a class=\"read-more\" href=\"https:\/\/www.aiproblog.com\/index.php\/2020\/12\/07\/researchers-can-use-qsim-to-explore-quantum-algorithms\/\">Read More<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":4173,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[24],"tags":[],"_links":{"self":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/4172"}],"collection":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/comments?post=4172"}],"version-history":[{"count":0,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/4172\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media\/4173"}],"wp:attachment":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media?parent=4172"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/categories?post=4172"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/tags?post=4172"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}