Statistical Problems in Large Volatility Matrix Estimation and Quantum Annealing Based Computing

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As the modern "data deluge" grows, the importance of statistics continues to increase, and uses of statistical methods rapidly evolve. The new era of data science poses great challenges to traditional statistical tools and computational techniques; yet, at the same time, the data deluge presents unprecedented opportunities to statistics. This project plans to advance research at the frontiers of science with innovative statistical and computational approaches that address the challenges encountered in handling complex problems with big data. The project's statistical research on quantum computing and high-frequency finance will address practical problems, and the projects will create advanced effective statistical tools with direct applications in fields including finance, quantum computation, and quantum information. This project seeks to conduct novel research on quantum annealing and statistical inference about large-dimensional matrices. The goals entail developing statistical methodologies, computing techniques, and theories for (i) statistical inference for large diffusion covariance matrices with applications to high-frequency finance, and (ii) statistical research on quantum annealing in quantum computation and quantum information. The project will develop rigorously-supported statistical methods and computational techniques, furthering the theoretical underpinning of these important topics.