{"id":24909,"date":"2024-12-09T11:46:37","date_gmt":"2024-12-09T11:46:37","guid":{"rendered":"https:\/\/www.qureca.com\/?post_type=tribe_events&p=24909"},"modified":"2024-12-09T11:46:37","modified_gmt":"2024-12-09T11:46:37","slug":"aiquantum-acp-2025","status":"publish","type":"tribe_events","link":"https:\/\/www.qureca.com\/fr\/calendar\/aiquantum-acp-2025\/","title":{"rendered":"AI+Quantum ACP 2025"},"content":{"rendered":"
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Artificial Intelligence (AI) and Quantum Science are among the most active areas in cutting-edge science and technology, with both addressing the computational complexity frontier. Although these two domains have evolved separately in the past, there are growing efforts to leverage recent breakthroughs in each field and tackle outstanding challenges through AI-Quantum synergy. The breakthroughs in language model (LLM) are enroute to establishing LLMs as new computational languages and breaking down barriers between domains. QI science is entering a new era, approaching error-corrected logical qubits and logical quantum processors, enabling quantum algorithms of unprecedented complexity. This conference aims to bring together researchers from academia and industry, leading the movement of AI-Quantum interdisciplinary research to address bottleneck issues and propel progress.\u00a0<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n

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Topics<\/span><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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Topics that will be covered at this conference include:<\/span><\/p>\n

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    Application of LLM for state characterization and error correction on quantum hardware.<\/span><\/p>\n<\/li>\n

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    Bootstrapping classical computing for quantum simulation.<\/span><\/p>\n<\/li>\n

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    Using quantum hardware to explore improvements in AI\u2019s learning dynamics.<\/span><\/p>\n<\/li>\n

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    Using Quantum many-body physics research tasks as a testing ground for LLM\u2019s.<\/span><\/p>\n<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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    Confirmed Speakers<\/span><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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      Adam Brown,\u00a0<\/span><\/span>Google Deep Mind<\/span><\/p>\n<\/li>\n

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      Ben Lev,\u00a0<\/span><\/span>Stanford University<\/span><\/p>\n<\/li>\n

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      David Pfau,\u00a0<\/span><\/span>Google Deep Mind<\/span><\/p>\n<\/li>\n

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      Di Luo,\u00a0<\/span><\/span>UCLA<\/span><\/p>\n<\/li>\n

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      Eva Silverstein,\u00a0<\/span><\/span>Stanford University<\/span><\/p>\n<\/li>\n

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      Hsin-Yuan (Robert) Huang,\u00a0<\/span><\/span>Caltech<\/span><\/p>\n<\/li>\n

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      Ichiro Takeuchi,\u00a0<\/span><\/span>University of Maryland<\/span><\/p>\n<\/li>\n

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      Jungsang Kim,\u00a0<\/span><\/span>Duke University<\/span><\/p>\n<\/li>\n

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      Khadijeh (Sona) Najafi,<\/span>\u00a0<\/span>IBM Quantum<\/span><\/p>\n<\/li>\n

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      Maissam Barkeshli,\u00a0<\/span><\/span>University of Maryland<\/span><\/p>\n<\/li>\n

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      Michael Trott,\u00a0<\/span><\/span>Wolfram Research<\/span><\/p>\n<\/li>\n

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      Mikhail Lukin,\u00a0<\/span><\/span>Harvard University<\/span><\/p>\n<\/li>\n

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      Paul Ginsparg,\u00a0<\/span><\/span>Cornell University<\/span><\/p>\n<\/li>\n

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      Roger Melko,\u00a0<\/span><\/span>University of Waterloo<\/span><\/p>\n<\/li>\n

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      Soonwon Choi,\u00a0<\/span><\/span>MIT<\/span><\/p>\n<\/li>\n

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      Steven Wolfram,\u00a0<\/span><\/span>Wolfram Research<\/span><\/p>\n<\/li>\n

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      Yasaman Bahri,\u00a0<\/span><\/span>Google\u00a0<\/span><\/span>Deep Mind<\/span><\/p>\n<\/li>\n

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      Yi-Zhuang You,\u00a0<\/span><\/span>UCSD<\/span><\/p>\n<\/li>\n

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      Yuri Lensky,\u00a0<\/span><\/span>Google Quantum AI<\/span><\/p>\n<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n","protected":false},"excerpt":{"rendered":"

      Artificial Intelligence (AI) and Quantum Science are among the most active areas in cutting-edge science and technology, with both addressing the computational complexity frontier. 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