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

• 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

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灵感，以团队为基础，多学科的努力，解决国家重要性的挑战，并将在不久的将来产生对社会有价值的可交付成果。这笔拨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。人工智能的一个新分支，生成对抗网络（GAN），有望探索一个大的化学空间，并产生针对某种疾病的新型候选药物。量子GAN （QGAN）作为加速经典GAN的一种途径而出现。该项目将创建一个实验性量子GAN框架，以探索噪声中尺度量子（NISQ）计算机上的化合物。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。这个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nisq时代的计算机充分利用量子优势进行药物发现？我们应该将量子资源用于搜索、辨别和奖励强化，还是将其全部分配给搜索，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？（三）能否加强QGAN中的量子分析，探索化学空间中与药理学相关的领域？（iv）在训练和生成过程中噪声对QGAN的影响是什么？(v)是否有其他材料系统可以为现有易受噪音影响的量子位提供抗噪音能力？（vi）在探索量子计算的发现应用方面，我们能为用户提供怎样的灵活性？主要任务集中在开发软件工具链，以弥合量子人工智能算法和硬件之间的差距，使用NISQ计算机探索药物发现，并准备量子智能和多样化的劳动力。对K-12教师的拓展将包括一个专业发展研讨会和与量子计算入门内容相关的课程材料。该奖项反映了美国国家科学基金会的法定使命，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。

项目成果

Activation of Disulfide Redox Switch in REDD1 Promotes Oxidative Stress Under Hyperglycemic Conditions.

• DOI: 10.2337/db22-0355
• 发表时间: 2022-12-01
• 期刊: Diabetes
• 影响因子: 7.7

Split Compilation for Security of Quantum Circuits

量子电路安全的分割编译

• DOI: 10.1109/iccad51958.2021.9643478
• 发表时间: 2021
• 期刊: 10.1109/ICCAD51958.2021.9643478
• 作者: Saki, Abdullah Ash;Suresh, Aakarshitha;Topaloglu, Rasit Onur;Ghosh, Swaroop
• 通讯作者: Ghosh, Swaroop

Quantum Computing at the Intersection of Engineering, Technology, Science, and Societal Need: Design of NGSS-aligned Quantum Drug Discovery Lessons for Middle School Students

工程、技术、科学和社会需求交叉点的量子计算：为中学生设计符合 NGSS 的量子药物发现课程

• 发表时间: 2021
• 期刊: Middle Atlantic ASEE Section Spring 2021 Conference
• 作者: Amy Voss Farris, Anna Eunji

Trainable PQC-Based QRAM for Quantum Storage

用于量子存储的可训练的基于 PQC 的 QRAM

• DOI: 10.1109/access.2023.3278600
• 发表时间: 2023
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Phalak, Koustubh;Li, Junde;Ghosh, Swaroop
• 通讯作者: Ghosh, Swaroop

A Survey and Tutorial on Security and Resilience of Quantum Computing

量子计算安全性和弹性的调查和教程

• 发表时间: 2021
• 期刊: ieee european test symposium
• 作者: Abdullah Ash Saki, Mahabubul Alam