NSF Convergence Accelerator - Track C: SQAI: Scalable Quantum Artificial Intelligence for Discovery

NSF 融合加速器 - 轨道 C：SQAI：用于发现的可扩展量子人工智能

基本信息

批准号：
2040667
负责人：
Swaroop Ghosh
金额：
$ 96万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-15 至 2023-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040667&HistoricalAwards=false
关键词：
NSF Convergence Accelerator Track SQAI

项目摘要

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. This grant will benefit society by compressing the pharmaceutical discovery timeline and reducing cost. This would have societal impacts both economically and in human health. A new branch of artificial intelligence, Generative Adversarial Networks (GAN), shows promise for exploring a large chemical space and generating novel pharmaceutical candidates targeted for a certain disease. Quantum GAN (QGAN) has emerged as a path to accelerate classical GANs. This project will create an experimental quantum GAN framework to explore chemical compounds on Noisy Intermediate Scale Quantum (NISQ) computers. This will yield a new discovery-based framework, Scalable Quantum Artificial Intelligence (SQAI) which could be employed for other applications.This Convergence Accelerator team will answer fundamental questions in the context of drug discovery such as: (i) How to exploit quantum advantage to the fullest for drug discovery using NISQ-era computers? Should we use quantum resources for search, discrimination and reinforcement of reward or allocate them fully for search and rely on classical paradigm for regular tasks? (ii) Does a particular molecular representation benefit NISQ-era computers? (iii) Can we enhance the quantum ansatz in QGAN to explore pharmacologically relevant areas of chemical space? (iv) What is the implication of noise on QGAN during training and generation? (v) Are there other material systems that will provide noise immunity to the existing noise-prone qubits? (vi) What kind of flexibility can we offer to the users in exploring quantum computing for their discovery application? The main tasks are focused on developing a software toolchain to bridge the gap between quantum AI algorithms and hardware, exploring drug discovery using NISQ computers, and preparing a quantum smart and diverse workforce. Outreach to K-12 teachers will include a professional development workshop and curricular materials related to introductory quantum computing content.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.

NSF 融合加速器支持以使用为基础的、基于团队的多学科努力，以解决国家重大挑战，并将在不久的将来为社会提供有价值的成果。这笔赠款将通过压缩药物发现时间表和降低成本来造福社会。这将对经济和人类健康产生社会影响。生成对抗网络（GAN）是人工智能的一个新分支，它有望探索广阔的化学空间并生成针对某种疾病的新型候选药物。量子 GAN (QGAN) 已成为加速经典 GAN 的一条途径。该项目将创建一个实验性量子 GAN 框架，以在嘈杂的中级量子 (NISQ) 计算机上探索化合物。这将产生一个新的基于发现的框架，可扩展量子人工智能（SQAI），可用于其他应用。该融合加速器团队将回答药物发现背景下的基本问题，例如：（i）如何利用量子优势使用 NISQ 时代的计算机来充分进行药物发现？我们应该使用量子资源来搜索、区分和强化奖励，还是将它们完全分配给搜索并依赖经典范式来完成常规任务？ (ii) 特定的分子表示是否有利于 NISQ 时代的计算机？ (iii) 我们能否增强 QGAN 中的量子模拟来探索化学空间的药理学相关领域？ (iv) 训练和生成过程中噪声对 QGAN 有何影响？ (v) 是否有其他材料系统可以为现有的易受噪声影响的量子位提供抗噪声能力？ (vi) 我们可以为用户探索量子计算的发现应用提供什么样的灵活性？主要任务集中于开发软件工具链以弥合量子人工智能算法和硬件之间的差距，利用 NISQ 计算机探索药物发现，并准备一支量子智能和多元化的劳动力队伍。对 K-12 教师的推广将包括专业发展研讨会和与量子计算入门内容相关的课程材料。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。