ATD: Quantum algorithms for spatiotemporal models with applications to threat detection

ATD：时空模型的量子算法及其在威胁检测中的应用

• 批准号: 2319279
• 负责人: Ping Ma
• 金额: $ 15万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319279&HistoricalAwards=false
• 关键词: ATD; Quantum; algorithms; spatiotemporal; models

Human dynamics are used to seek comprehension of human behaviors by employing statistical models. Research has demonstrated that certain human behaviors can be quantitatively modeled using proxy tools such as social media. The field of human dynamics has gained significant attention in the realm of security and defense, not only for its potential to detect anomalies in human behavior but also for its capacity to mitigate potential catastrophic damage and societal distress. Despite the pressing need, state-of-the-art computational tools for studying human dynamics are still lacking. However, classic models alone are insufficient for capturing the constantly evolving spatial and temporal trends in human dynamics. Additionally, the computational cost of spatiotemporal models on classical computers is prohibitively high, posing challenges for real-time analysis of human dynamics data. Recent advancements in quantum computing have showcased quantum supremacy, wherein quantum computers outperform classical computers in some problem-solving. Quantum parallelism, in particular, bypasses the time/space trade-off associated with classical parallel computing, thanks to its ability to store exponentially many units of information within a linear physical space. Moreover, quantum computers possess logic gates that classical computers lack, enabling faster computations. However, the achievements of quantum computing in the literature have been predominantly limited to physics-oriented problems and have not garnered much attention from the data science community. In this project, our aim is to harness the power of quantum algorithms for modeling human dynamics to enhance threat detection capabilities. Our proposed approaches are general quantum computing tools that are widely applicable. The proposed framework (i) can be used to discover unusual events in any super-large data set, (ii) inspires a new line of research in quantum computing, and (iii) offers a unique opportunity for students to participate in cutting-edge and interdisciplinary big data research.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.

人类动态用于通过采用统计模型来寻求对人类行为的理解。研究表明，可以使用社交媒体等代理工具定量建模某些人类行为。人类动态领域在安全和防御领域引起了人们的重大关注，这不仅是因为它有潜在地检测人类行为异常，而且还因为它可以减轻潜在的灾难性损害和社会困扰。尽管需要紧迫，但仍缺乏用于研究人类动态的最新计算工具。但是，仅经典模型就不足以捕获人类动态中不断发展的空间和时间趋势。此外，古典计算机上时空模型的计算成本非常高，这对人类动力学数据的实时分析提出了挑战。 量子计算的最新进展展示了量子至上，其中量子计算机在某些问题解决方面的表现优于古典计算机。量子并行性，尤其是绕过与经典并行计算相关的时间/空间权衡，这要归功于其在线性物理空间中以指数级存储许多信息单位的能力。此外，量子计算机具有古典计算机所缺乏的逻辑门，可以更快地计算。但是，文献中量子计算的成就主要仅限于面向物理的问题，并且没有引起数据科学界的太多关注。在这个项目中，我们的目的是利用量子算法的力量来建模人类动态以增强威胁检测能力。 我们提出的方法是广泛适用的通用量子计算工具。拟议的框架（i）可用于在任何超大数据集中发现异常事件，（ii）启发了量子计算方面的新研究，（iii）为学生提供了一个独特的机会，让学生参与尖端和跨学科的大数据研究。该奖项反映了NSF的法定任务，并被认为是使用基金会的知识分子优点和更广泛影响的评论标准的评估值得支持的。