{"id":1276,"date":"2022-12-02T16:39:48","date_gmt":"2022-12-02T14:39:48","guid":{"rendered":"https:\/\/webs.uab.cat\/giq\/seminar\/a-learning-theory-for-quantum-photonic-processors-and-beyond\/"},"modified":"2022-12-02T16:39:48","modified_gmt":"2022-12-02T14:39:48","slug":"a-learning-theory-for-quantum-photonic-processors-and-beyond","status":"publish","type":"seminar","link":"https:\/\/webs.uab.cat\/giq\/seminar\/a-learning-theory-for-quantum-photonic-processors-and-beyond\/","title":{"rendered":"A learning theory for quantum photonic processors and beyond"},"content":{"rendered":"<div style=\"font-family:comic sans ms,sans-serif;font-size:small\">We consider a family of Gaussian and non-Gaussian continuous-variable (CV) circuits, suitable to describe state-of-the-art photonic processors, and evaluate its learning capabilities.<br \/>\nOur basic learning problem is: given copies of an unknown quantum state, we apply to each of them a random quantum circuit from a given set C and obtain measurement outcomes. The objective is to approximate the unknown state using a state from a given hypothesis set S, using a small number of samples.&nbsp; We show that a good approximation can be found with a number of samples polynomial in the number of modes of the CV circuit, which is a measure of its<br \/>\nsize. We apply our results to learning a decoder for optical communication, outperforming the state of the art. Ref. <a data-saferedirecturl=\"https:\/\/www.google.com\/url?q=https:\/\/arxiv.org\/abs\/2209.03075&amp;source=gmail&amp;ust=1670081783391000&amp;usg=AOvVaw24Gn4pBr45h-KHGuLheBRG\" href=\"https:\/\/arxiv.org\/abs\/2209.03075\" target=\"_blank\" rel=\"noopener\">https:\/\/arxiv.org\/abs\/2209.<wbr \/>03075<\/a><\/div>\n<div style=\"font-family:comic sans ms,sans-serif;font-size:small\">&nbsp;<\/div>\n","protected":false},"excerpt":{"rendered":"<p>We consider a family of Gaussian and non-Gaussian continuous-variable (CV) circuits, suitable to describe state-of-the-art photonic processors, and evaluate its learning capabilities. Our basic learning problem is: given copies of an unknown quantum state, we apply to each of them a random quantum circuit from a given set C and obtain measurement outcomes. The objective [&hellip;]<\/p>\n","protected":false},"author":20,"featured_media":0,"template":"","class_list":["post-1276","seminar","type-seminar","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/webs.uab.cat\/giq\/wp-json\/wp\/v2\/seminar\/1276","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/webs.uab.cat\/giq\/wp-json\/wp\/v2\/seminar"}],"about":[{"href":"https:\/\/webs.uab.cat\/giq\/wp-json\/wp\/v2\/types\/seminar"}],"author":[{"embeddable":true,"href":"https:\/\/webs.uab.cat\/giq\/wp-json\/wp\/v2\/users\/20"}],"wp:attachment":[{"href":"https:\/\/webs.uab.cat\/giq\/wp-json\/wp\/v2\/media?parent=1276"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}