I have long been interested in applying quantum chemistry methods originally developed for molecules to solids and materials. This is an exciting opportunity because many of those quantum chemistry methods have not yet been applied to solids and we do not know how well or poorly they work for solids yet. In solid-state applications, in addition to reaching the basis set limit, accessing the thermodynamic limit (i.e., infinite system size limit) is critical. Reaching both basis set and thermodynamic limits poses greater challenges to existing quantum chemistry approaches and the computational cost reduction becomes even more important than it was for molecules. I am interested in a broad range of approaches including density functional theory, Moller-Plesset perturbation theory, coupled-cluster theory, and auxiliary-field quantum Monte Carlo. I also maintain an interest in embedding approaches in this context.

Several years ago, I started to think about how to harness the power of quantum computers to solve difficult problems in quantum chemistry, condensed matter physics, and materials science. On Noisy Intermediate-Scale Quantum (NISQ) machines, it has been difficult to realize practical quantum advantage over classical state-of-the-art quantum chemistry methods that I use and develop in my other research projects. To this end, I have recently proposed a new quantum-classical hybrid algorithm that shows some potential for scalability and accuracy even with NISQ machines. In particular, with collaborators at Google, I demonstrated that we can already compute the ground state energy accurately up to systems with 16-qubit, which is the largest chemistry simulation on a quantum computer so far. I am interested in developing this approach further as a means to potentially demonstrate a quantum advantage in quantum chemistry. Aside from near-term algorithmic developments, I am also interested in quantum dynamics problems with fault-tolerant machines and the ground state computation with quantum phase estimation.

My other research interests are centered on methodology developments, but all of them are for tackling interesting problems in chemistry and materials. These problems are very difficult (or impossible) to solve with currently available tools and our understanding of them is very limited. Just to name a few, I became interested in the behavior of correlated metal oxides from ab-initio calculations due to their application in magnetic storage, understanding the transport properties such as mobility of lead-halide perovskites (a highly promising solar material), understanding the exciton condensation phenomenon observed in transition metal dichalcogenides, and emerging correlated phases in Moire materials.

The development of production-level programs plays a major role in computational science. Such tools allow for testing new ideas with as little pain as possible and once validated implementing these new ideas efficiently for large-scale applications. I have been an active developer of Q-Chem, a commercial quantum chemistry package, for many years. Furthermore, I am the lead developer of a Python-based auxiliary-field quantum Monte Carlo code called ipie. I hope to continue this software development.