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<\/a><\/p>\nThe result could have a huge impact on how America uses energy.<\/p>\n
\u201cAnytime you want to convert energy between electrical forms and mechanical forms, like in wind turbines, electric vehicles or hydro power, you need to have a magnet that mediates that process,\u201d said LLNL scientist and project lead Ilon Joseph. \u201cIf we can do much better calculations of magnetic materials science, we can find new kinds of magnetic materials that can power our energy technology.\u201d<\/p>\n
New magnet materials could circumvent China\u2019s critical material supply chain and offer improvements in terms of weight, strength, robustness and resistance to corrosion.<\/p>\n
Even slight enhancements could also decrease the resources needed to power artificial intelligence (AI) and information technology (IT). Much of the energy consumption in AI and IT comes from writing and erasing information stored in memory. For MRAM-based chips, which store data using magnetic states, reading and writing requires flipping the magnetization of tiny thin-film magnets. Because AI and IT are predicted to dominate U.S. electricity consumption by the end of the decade, magnetic memory that takes less energy to flip \u2014 even by 20% \u2014 would lower energy costs significantly.<\/p>\n
To discover these new magnetic materials, the team at LLNL is combining various fields of expertise. Researchers at the Laboratory created some of the most advanced codes in the world for simulating electronic structure and realistic materials at the atomic scale. Those tools currently run on El Capitan, the most powerful supercomputer in the world.<\/p>\n
\u201cWe will connect our state-of-the-art electronic structure simulation code running on high-performance computing systems, such as El Capitan, and offload hard quantum aspects of the problem to quantum frameworks,\u201d said LLNL scientist Alfredo Correa Tedesco. \u201cOf course, making those quantum resources work is the most challenging part \u2014 but it is also where we have the most to gain in terms of capabilities.\u201d<\/p>\n
Adapting these materials simulations to run on a quantum computer will offer even better performance. The magnetic spins present in a material represent a many-body quantum system, and, while modeling them with a classical computer is challenging, modeling them with a quantum computer is efficient \u2014 a natural fit.<\/p>\n
However, almost none of the algorithms that we use on today\u2019s classical computing hardware will be good for quantum computers. LLNL\u2019s main task lies in the translation from the classical to the quantum algorithm. For example, Joseph has a track record of developing efficient quantum algorithms for solving the partial differential equations needed to simulate fluids and plasmas. He will focus on developing and implementing efficient quantum algorithms for the direct simulation of quantum magnets.<\/p>\n
For useful quantum calculations, the scientists will need to focus on quantum error correction, which is essential to obtain a realistic calculation that beats a classical computer. With many physical qubits \u2014 on the order of 10,000 \u2014 they plan to group them together and create enough redundancy to generate 100 so-called \u201clogical qubits\u201d. While some of the physical qubits might be wrong, the error correction protocol ensures that the physical calculation comes together to form a correct solution in terms of logical qubits.<\/p>\n
That requires significant quantum hardware that, as of today, is not yet available. The LLNL researchers expect to begin working with a prototype from their hardware partner, one of the leaders in the field of neutral atom computing, in about a year. Then they\u2019ll have the remaining two years of the project to make their algorithm work, tying the results of the quantum computation to a machine-learning algorithm that will flag magnetic materials with the potential to transform the energy landscape.<\/p>\n
\u201cThis is a project that\u2019s almost on the edge of the impossible. We\u2019re on the cusp,\u201d said Joseph. \u201cBut even if we fail, if we can prove we are on the path to making a quantum computer that can do these calculations within the next 2-3 years, that will be a major victory.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"Insider Brief Lawrence Livermore National Laboratory (LLNL) will lead a $4.1M ARPA-E project to develop quantum and machine…\n","protected":false},"author":2,"featured_media":615344,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[49],"tags":[199,79],"class_list":["post-615343","post","type-post","status-publish","format-standard","has-post-thumbnail","category-physics","tag-physics","tag-science"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/posts\/615343","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/comments?post=615343"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/posts\/615343\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/media\/615344"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/media?parent=615343"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/categories?post=615343"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/us\/wp-json\/wp\/v2\/tags?post=615343"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}