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.

人类动力学用于通过统计模型来寻求对人类行为的理解。研究表明，某些人类行为可以使用社交媒体等代理工具进行定量建模。人类动力学领域在安全和国防领域获得了极大的关注，不仅因为它具有检测人类行为异常的潜力，而且还因为它能够减轻潜在的灾难性损害和社会痛苦。尽管需求迫切，但仍然缺乏用于研究人类动力学的最先进的计算工具。然而，仅靠经典模型不足以捕捉人类动态中不断变化的空间和时间趋势。此外，经典计算机上时空模型的计算成本非常高，这给人体动力学数据的实时分析带来了挑战。 量子计算的最新进展展示了量子霸权，其中量子计算机在某些问题解决方面优于经典计算机。特别是，量子并行性绕过了与经典并行计算相关的时间/空间权衡，因为它能够在线性物理空间内存储指数级的多个信息单位。此外，量子计算机拥有传统计算机所缺乏的逻辑门，可以实现更快的计算。然而，文献中量子计算的成就主要局限于面向物理的问题，并没有引起数据科学界的太多关注。在这个项目中，我们的目标是利用量子算法的力量来建模人体动力学，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）可用于发现任何超大型数据集中的异常事件，（ii）激发量子计算的新研究方向，（iii）为学生提供参与前沿和跨学科大数据研究的独特机会。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。