{"id":20026,"date":"2025-01-21T19:05:02","date_gmt":"2025-01-21T18:05:02","guid":{"rendered":"https:\/\/telecomkh.info\/?p=20026"},"modified":"2025-01-21T19:05:02","modified_gmt":"2025-01-21T18:05:02","slug":"nvidia-accelerates-google-quantum-ai-processor-design-with-simulation-of-quantum-device-physics","status":"publish","type":"post","link":"https:\/\/telecomkh.info\/?p=20026","title":{"rendered":"NVIDIA accelerates Google Quantum AI processor design with simulation of quantum device physics"},"content":{"rendered":"<p><strong>NVIDIA CUDA-Q platform enables Google Quantum AI researchers to create massive digital model of its quantum computer to solve design challenges<\/strong><\/p>\n<p>NVIDIA announced it is working with Google Quantum AI to accelerate the design of its next-generation quantum computing devices using simulations powered by the NVIDIA CUDA-Q&#x2122; platform.<br \/>\nGoogle Quantum AI is using the hybrid quantum-classical computing platform and the NVIDIA Eos supercomputer to simulate the physics of its quantum processors. This will help overcome the current limitations of quantum computing hardware, which can only run a certain number of quantum operations before computations must cease, due to what researchers call \u201cnoise.\u201d<br \/>\n\u201cThe development of commercially useful quantum computers is only possible if we can scale up quantum hardware while keeping noise in check,\u201d said Guifre Vidal, research scientist from Google Quantum AI. \u201cUsing NVIDIA accelerated computing, we\u2019re exploring the noise implications of increasingly larger quantum chip designs.\u201d<br \/>\nUnderstanding noise in quantum hardware designs requires complex dynamical simulations capable of fully capturing how qubits within a quantum processor interact with their environment.<br \/>\nThese simulations have traditionally been prohibitively computationally expensive to pursue. Using the CUDA-Q platform, however, Google can employ 1,024 NVIDIA H100 Tensor Core GPUs at the NVIDIA Eos supercomputer to perform one of the world\u2019s largest and fastest dynamical simulation of quantum devices \u2014 at a fraction of the cost.<br \/>\n\u201cAI supercomputing power will be helpful to quantum computing\u2019s success,\u201d said Tim Costa, director of quantum and HPC at NVIDIA. \u201cGoogle\u2019s use of the CUDA-Q platform demonstrates the central role GPU-accelerated simulations have in advancing quantum computing to help solve real-world problems.\u201d<br \/>\nWith CUDA-Q and H100 GPUs, Google can perform fully comprehensive, realistic simulations of devices containing 40 qubits \u2014 the largest-performed simulations of this kind. The simulation techniques provided by CUDA-Q mean noisy simulations that would have taken a week can now run in minutes.<br \/>\nThe software powering these accelerated dynamic simulations will be publicly available in the CUDA-Q platform, allowing quantum hardware engineers to rapidly scale their system designs.<\/p>\n<p><span style=\"color: #999999;\"><em>Above, Google Quantum Computer \/ image credited to Google<\/em><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>NVIDIA CUDA-Q platform enables Google Quantum AI researchers to create massive digital model of its quantum computer to solve design challenges NVIDIA announced it is working with Google Quantum AI to accelerate the design of its next-generation quantum computing devices using simulations powered by the NVIDIA CUDA-Q&#x2122; platform. Google Quantum AI is using the hybrid &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/telecomkh.info\/?p=20026\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> \u00abNVIDIA accelerates Google Quantum AI processor design with simulation of quantum device physics\u00bb<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":20027,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[69],"tags":[],"_links":{"self":[{"href":"https:\/\/telecomkh.info\/index.php?rest_route=\/wp\/v2\/posts\/20026"}],"collection":[{"href":"https:\/\/telecomkh.info\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/telecomkh.info\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/telecomkh.info\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/telecomkh.info\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=20026"}],"version-history":[{"count":1,"href":"https:\/\/telecomkh.info\/index.php?rest_route=\/wp\/v2\/posts\/20026\/revisions"}],"predecessor-version":[{"id":20028,"href":"https:\/\/telecomkh.info\/index.php?rest_route=\/wp\/v2\/posts\/20026\/revisions\/20028"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/telecomkh.info\/index.php?rest_route=\/wp\/v2\/media\/20027"}],"wp:attachment":[{"href":"https:\/\/telecomkh.info\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=20026"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/telecomkh.info\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=20026"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/telecomkh.info\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=20026"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}