The same type of artificial intelligence that mastered the ancient game of Go could help wrestle with the amazing complexity of quantum systems containing billions of particles.<\/p>\n
Google\u2019s AlphaGo artificial neural network made headlines last year when it bested a world champion at Go. After marvelling at this feat, Giuseppe Carleo of ETH Zurich in Switzerland thought it might be possible to build a similar machine-learning tool to crack one of the knottiest problems in quantum physics.<\/p>\n
Now, he has built just such a neural network \u2013 which could turn out to be a game changer in understanding quantum systems.<\/p>\n
Go is far more complex than chess, in that the number of possible positions on a Go board could exceed the number of atoms in the universe. That\u2019s why an approach based on brute-force calculation, while effective for chess, just doesn\u2019t work for Go.<\/p>\n
In that sense, Go resembles a classic problem in quantum physics: how to describe a quantum system that consists of many billions of atoms, all of which interact with each other according to complicated equations.<\/p>\n
But the weird rules of quantum mechanics mean we can\u2019t know a quantum particle\u2019s precise location at every point in time. Many quantum particles also have a property called \u201cspin\u201d, which can be either up or down. The number of spin-based states that a group of just 100 such particles could inhabit is almost a million trillion trillion (1030).<\/p>\n
The current record for simulating such a system, using our most powerful supercomputers, is 48 spins. Carleo estimates that even if we could turn the entire planet into a giant hard drive, we would still only be able to do these calculations for 100 spins at most.<\/p>\n
That\u2019s where artificial neural networks can help. Give such a network the rules of Go and it will figure out the optimal strategy to win the game. So perhaps it could do the same for quantum systems.<\/p>\n “<\/span>Neural networks are very good at generalising, so they typically only need a limited amount of information to infer much more from that.”<\/span> <\/blockquote> Feed a neural network a few pictures of Carleo, for example, and it will soon be able to recognise him in new pictures it has never \u201cseen\u201d before.<\/p>\n “<\/span>It\u2019s incredibly cool … given the success of deep learning in pretty much every imaginable application domain, it\u2019s a natural idea to try it for quantum many-body physics, but as far as I know this is the first time someone did. I expect to see a lot more of this in the future.”<\/span> <\/blockquote> \tRead More<\/a> The same type of artificial intelligence that mastered the ancient game of Go could help…<\/p>\n","protected":false},"author":1,"featured_media":2336,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[3],"tags":[4366,124,1421,4367,2046,10,482],"class_list":["post-1609","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","tag-learns","tag-many","tag-once","tag-particles","tag-quantum","tag-solve","tag-state"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/techclot.com\/wp-content\/uploads\/2020\/08\/4G1jb9.jpg?fit=500%2C325&ssl=1","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p3orZX-pX","jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/posts\/1609","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/comments?post=1609"}],"version-history":[{"count":0,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/posts\/1609\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/media\/2336"}],"wp:attachment":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/media?parent=1609"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/categories?post=1609"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/tags?post=1609"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\n2019 Artificial Intelligence News – AI News<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"