Garnet Chan's research lies at the interface of theoretical chemistry, condensed matter physics, and quantum information theory, and is concerned with quantum many-particle phenomena and the numerical methods to simulate them. The aim is to understand physical systems at the boundaries of accessible computational complexity, and to devise new physical simulation methods to push these boundaries forward. Over the last decade, his group has contributed to and invented a variety of methods addressing different aspects of quantum simulations, ranging from the challenges of strong electron correlation, to treating many-particle problems in the condensed phase, to dynamical simulations of spectra and coupling between electron and nuclear degrees of freedom. Some of these methods include density matrix renormalization and tensor network algorithms for real materials, canonical transformation-based down-foldings, local quantum chemistry methods, quantum embeddings including dynamical mean-field theory and density matrix embedding theory, and new quantum Monte Carlo algorithms. The primary focus is on methodologies for problems which appear naively exponentially hard, but where an understanding of inherent physics, for example in terms of the entanglement structure, allows for calculations of polynomial cost.
Instructors: Chan (a), Wei (b), Beauchamp (c)