{"id":1543,"date":"2022-03-21T22:26:43","date_gmt":"2022-03-22T02:26:43","guid":{"rendered":"https:\/\/quantum.ncsu.edu\/ibm-quantum\/?post_type=tribe_events&#038;p=1543"},"modified":"2022-04-18T12:44:53","modified_gmt":"2022-04-18T16:44:53","slug":"triangle-quantum-computing-seminar-2","status":"publish","type":"tribe_events","link":"https:\/\/quantum.ncsu.edu\/ibm-quantum\/event\/triangle-quantum-computing-seminar-2\/","title":{"rendered":"Triangle Quantum Computing Seminar\u00a0"},"content":{"rendered":"<h3><strong>Join Today&#8217;s Duke, NC State and UNC collaborative Triangle Quantum Computing Seminar\u00a0<\/strong><\/h3>\n<p><strong>Talk Title: <\/strong>Generative Quantum Learning of Joint Probability Distribution Functions<\/p>\n<p><strong>Speaker: Elton Zhu<\/strong><\/p>\n<p><strong>Hosted by:<\/strong>\u00a0 IBM Quantum Hub at NC State<\/p>\n<p><strong>Abstract:\u00a0\u00a0<\/strong>Modeling joint probability distributions is an important task in a wide variety of fields. One popular technique for this employs a family of multivariate distributions with uniform marginals called copulas. While the theory of modeling joint distributions via copulas is well understood, it gets practically challenging to accurately model real data with many variables. In this work, we show that any copula can be naturally mapped to a multipartite maximally entangled state. Thus, the task of learning joint probability distributions becomes the task of learning maximally entangled states. We prove that a variational ansatz we christen as a `qopula&#8217; based on this insight leads to an exponential advantage over classical methods of learning some joint distributions. As an application, we train a Quantum Generative Adversarial Network (QGAN) and a Quantum Circuit Born Machine (QCBM) using this variational ansatz to generate samples from joint distributions of two variables in historical data from the stock market. We demonstrate our generative learning algorithms on trapped ion quantum computers from IonQ for up to 8 qubits. Our experimental results show remarkable findings such as the resilience against noise, outperformance against equivalent classical models and 20-1000 times less iterations required to converge as compared to equivalent classical models.<\/p>\n<p>&nbsp;<\/p>\n<p><strong>This is a Hybrid Event with NC State.<\/strong><\/p>\n<p><strong>The In Person location for NC State is Venture Place, 2nd Floor, Large Classroom.<\/strong><\/p>\n<p><strong>REGISTRATION: <\/strong><a href=\"https:\/\/docs.google.com\/forms\/d\/1g1ulVXGcctMsWNwyoIlv36nfA9do7CB0eah_99h7xPU\/edit?usp=sharing\">Form Link\u00a0<\/a><\/p>\n<p>For more information email:\u00a0 quantumcomputing@ncsu.edu<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Join Today&#8217;s Duke, NC State and UNC collaborative Triangle Quantum Computing Seminar\u00a0 Talk Title: Generative Quantum Learning of Joint Probability Distribution Functions Speaker: Elton Zhu Hosted by:\u00a0 IBM Quantum Hub&hellip;<\/p>\n","protected":false},"author":43,"featured_media":0,"template":"","meta":{"_acf_changed":false,"ncst_custom_author":"","ncst_show_custom_author":false,"ncst_dynamicHeaderBlockName":"","ncst_dynamicHeaderData":"","ncst_content_audit_freq":"","ncst_content_audit_date":"","ncst_content_audit_display":false,"ncst_backToTopFlag":"","_tribe_events_status":"","_tribe_events_status_reason":"","_tribe_events_is_hybrid":"","_tribe_events_is_virtual":"","_tribe_events_virtual_video_source":"","_tribe_events_virtual_embed_video":"","_tribe_events_virtual_linked_button_text":"","_tribe_events_virtual_linked_button":"","_tribe_events_virtual_show_embed_at":"","_tribe_events_virtual_show_embed_to":[],"_tribe_events_virtual_show_on_event":"","_tribe_events_virtual_show_on_views":"","_tribe_events_virtual_url":"","footnotes":""},"tags":[28],"tribe_events_cat":[22],"class_list":["post-1543","tribe_events","type-tribe_events","status-publish","hentry","tag-triangle-quantum-computing-seminar","tribe_events_cat-quantum-computing-seminar","cat_quantum-computing-seminar"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.5 - 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