{"id":132302,"date":"2021-12-12T18:24:07","date_gmt":"2021-12-13T02:24:07","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2021\/12\/breakthrough-proof-clears-path-for-quantum-ai-overcoming-threat-of-barren-plateaus"},"modified":"2021-12-12T18:24:07","modified_gmt":"2021-12-13T02:24:07","slug":"breakthrough-proof-clears-path-for-quantum-ai-overcoming-threat-of-barren-plateaus","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2021\/12\/breakthrough-proof-clears-path-for-quantum-ai-overcoming-threat-of-barren-plateaus","title":{"rendered":"Breakthrough Proof Clears Path for Quantum AI \u2014 Overcoming Threat of \u201cBarren Plateaus\u201d"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/breakthrough-proof-clears-path-for-quantum-ai-overcoming-threat-of-barren-plateaus.jpg\"><\/a><\/p>\n<p><strong>Novel theorem demonstrates convolutional neural networks can always be trained on quantum computers, overcoming threat of \u2018barren plateaus\u2019 in optimization problems.<\/strong><\/p>\n<p>Convolutional neural networks running on quantum computers have generated significant buzz for their potential to analyze quantum data better than classical computers can. While a fundamental solvability problem known as \u201cbarren plateaus\u201d has limited the application of these neural networks for large data sets, new research overcomes that Achilles heel with a rigorous proof that guarantees scalability.<\/p>\n<p>\u201cThe way you construct a quantum neural network can lead to a barren plateau\u2014or not,\u201d said Marco Cerezo, coauthor of the paper titled \u201cAbsence of Barren Plateaus in Quantum Convolutional Neural Networks,\u201d published recently by a Los Alamos National Laboratory team in <em>Physical Review X<\/em>. Cerezo is a physicist specializing in quantum computing 0, quantum machine learning, and quantum information at Los Alamos. \u201cWe proved the absence of barren plateaus for a special type of quantum neural network. Our work provides trainability guarantees for this architecture, meaning that one can generically train its parameters.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Novel theorem demonstrates convolutional neural networks can always be trained on quantum computers, overcoming threat of \u2018barren plateaus\u2019 in optimization problems. Convolutional neural networks running on quantum computers have generated significant buzz for their potential to analyze quantum data better than classical computers can. While a fundamental solvability problem known as \u201cbarren plateaus\u201d has limited [\u2026]<\/p>\n","protected":false},"author":396,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1617,6],"tags":[],"class_list":["post-132302","post","type-post","status-publish","format-standard","hentry","category-quantum-physics","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/132302","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/396"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=132302"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/132302\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=132302"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=132302"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=132302"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}