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

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.