CDS&E: Two-electron Reduced Density Matrices in Quantum Chemistry and Physics

点击翻译按钮获取中文摘要

With this award, the Chemical Theory, Models and Computational Methods (CTMC) program in the Division of Chemistry is supporting Professor David A. Mazziotti of the University of Chicago to develop new methods for treating many-electron quantum systems to study problems in chemistry and materials. Many new molecules and materials that are critically important to improving society are so complex that they are difficult to study with conventional methods. Mazziotti and his research group will pursue novel quantum chemistry algorithms that exploit the pairwise nature of the electronic interactions in combination with recent advances in quantum computing, machine learning, and optimization to predict the energies and other properties of molecules and materials with increased efficiency. The work has the potential to be transformative in that it aims to enable applications to more complex chemical systems, opening new vistas for computational research in chemistry and materials. Mazziotti and his group will also drive educational and outreach activities including a quantum computing and machine learning curricula for chemistry, an interactive videoconference-based lecture series on quantum chemistry, and an online science journal for high school students. Accurate and yet efficient computation of molecules and materials with correlated electrons remains one of the outstanding problems in science and engineering. Catalyzed by recent discoveries in quantum computation, machine learning, and optimization theory, Mazziotti and his research group aim to improve predictive capabilities for correlated molecules and materials by exploring the combination of these recent advances with reduced density matrix (RDM) theory. They will develop RDM algorithms for general quantum systems with correlated electrons via new, emergent paradigms in quantum computing, machine learning, and/or optimization, going beyond recent classical algorithms based on approximate functionals or N-representability conditions. These novel formulations will use quantum computing, machine learning, and/or optimization to avoid the explicit classical construction and storage of the many-electron wave function, and therefore, they will raise new possibilities for treating strongly correlated systems that are beyond the reach of current approaches. The proposed work is timely: recently, quantum computers have been developed with significant improvements in their qubit-numbers and fidelity, and machine learning has been applied successfully to applications in image and speech recognition, natural language processing, autonomous driving, and chemistry. Mazziotti and his group will apply existing and proposed two-electron RDM methods to predict and optimize strongly correlated molecular systems and processes of importance in chemistry and physics.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.