PFI-TT: Prototyping a quantum-powered AI building platform

PFI-TT：量子驱动的人工智能构建平台原型设计

• 批准号: 2141058
• 负责人: German Kolmakov
• 金额: $ 25万
• 依托单位: CUNY New York City College of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2141058&HistoricalAwards=false
• 关键词: PFI; TT; Prototyping; quantum; powered

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is in significant improvement of artificial intelligence (AI) systems by acceleration of the model creation, training, and operation using modern quantum computers. The project's outcomes can stimulate advances in chemistry, create new materials and new drugs, improve manufacturing and retail, reduce risks in high-tech, exploration and mission planning, and improve decision-making in arenas such as the public sector, banking, and healthcare. The project will nurture a new generation of entrepreneurs and scientists who can move easily between disciplines as varied as engineering, physics, computer science, and high-performance simulations.The proposed project will develop an AI-building software platform that enables training the models “on the fly” on fast-evolving and fast-growing datasets. This will significantly improve their prediction and classification functions, reducing training time on large datasets by ten-fold. With the quantum-accelerated AI, a significant part of decision-making tasks can be automated to reduce human errors, thereby reducing mission failure risks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个创新-技术转化伙伴关系（PFI-TT）项目的更广泛影响/商业潜力是通过使用现代量子计算机加速模型创建，训练和操作来显着改进人工智能（AI）系统。该项目的成果可以刺激化学进步，创造新材料和新药，改善制造业和零售业，降低高科技，勘探和使命规划的风险，并改善公共部门，银行和医疗保健等领域的决策。该项目将培养新一代的企业家和科学家，他们可以轻松地在工程、物理、计算机科学和高性能模拟等不同学科之间移动。该项目将开发一个人工智能构建软件平台，使模型能够在快速发展和快速增长的数据集上“动态”训练。这将显著改善它们的预测和分类功能，将大型数据集的训练时间减少十倍。通过量子加速人工智能，可以自动执行很大一部分决策任务，以减少人为错误，从而降低使命失败风险。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。