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.

通过加速使用现代量子计算机的模型创建，培训和操作，这种伙伴关系对创新 - 技术翻译（PFI -TT）项目的更广泛的影响/商业潜力是显着改善人工智能（AI）系统。该项目的结果可以刺激化学的进步，创建新材料和新药物，改善制造业和零售，从而降低高科技，勘探和任务计划的风险，并改善公共部门，银行业和医疗保健等领域的决策。该项目将为新一代企业家和科学家提供护理，他们可以在工程，物理，计算机科学和高性能模拟等学科之间轻松移动。拟议的项目将开发一个AI构建软件平台，使其能够在快速变化和快速成长的数据集中培训模型“飞行”。这将大大提高其预测和分类功能，从而将大型数据集的培训时间减少十倍。借助量子加速的AI，可以自动化决策任务的很大一部分以减少人为错误，从而降低任务失败的风险。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的审查标准来评估的支持。