Numerical Quantum Simulations of Realistic Materials

Garnet Chan

Garnet Chan

Simulating quantum mechanics on classical computers appears at first to require exponential computational resources, yet at the same time rapid progress is being made in accurate simulations of the quantum properties of realistic materials. How is this discrepancy resolved? Professor Chan will explain why, for many purposes, the exponential complexity of quantum mechanics is an illusion, and how the simple structure of quantum states can be captured through different mathematical formalisms, including that of tensor networks. Chan will then briefly discuss how this translates into new tools of numerical simulation, and highlight applications to the first-principles study of biological systems, molecular crystals and high temperature superconductivity.

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Garnet K.-L. Chan is currently the Hepburn Professor of Theoretical Chemistry in the Department of Chemistry, Princeton University. He is also an associated faculty member of the Physics Department, and a faculty fellow of the Princeton Center for Theoretical Science. Prior to his appointment at Princeton, he was an associate professor of Chemistry and Chemical Biology at Cornell University. He has received a number of awards, including the ACS Award in Pure Chemistry, the Medal of the International Academy of Quantum Molecular Science, the Camille Dreyfus Teacher-Scholar Award, the Alfred P. Sloan and David and Lucile Packard fellowships, the NSF CAREER Award, and the Baker Award of the National Academy of Sciences.

His research lies at the interface of theoretical chemistry, condensed matter physics and quantum information theory, and is concerned with the phenomena and numerical methods associated with quantum many particle systems. Some current systems of interest include metalloenzymes and biological catalysts, transition metal oxides and superconductivity, and organic molecular crystals and light harvesting. He has contributed to a wide range of quantum simulation methods, including density matrix renormalization and tensor network algorithms for real materials, downfolding through canonical transformations, local quantum chemistry methods, quantum embeddings including dynamical mean-field theory and density matrix embedding theory, and new quantum Monte Carlo techniques.