I am a Physics PhD working in the Center for Simulational Physics in the Stancil group, focusing on quantum simulation of chemical dynamics. My quantum algorithms employ VQE for state preparation and use a variational fast-forwarding approach to compress terms of the trotterized Hamiltonian, optimized using quantum-classical hybrid techniques. The algorithms themselves are gnostic to hardware and easily configurable to maximize state fidelity on superconducting and trapped-ion systems. The goal of my research is to develop chemical dynamics algorithms that can be applied on fault-tolerant architectures and effectively exploit distributed resources. Such dynamics include real-time nonadiabatic dynamics, chemical scattering, and efficient extension to many-body and multi-channel problems. These systems can be analogized to risk analysis and option pricing in quantum finance and offer quantum advantage by the O(log(n)) size storage of internal states with polynomial depth and tunable approximability of dynamical systems.

Personal interests include graph-theoretic representations of qubit networks and topological changes that come with various qubit modalities, as well as broader applications of RMT in quantum information. Future directions for my research are the adaptation of SU(2) gauge theory in quantum simulation and real-time dynamics on lattice models. This has extended into quaternionic Hamiltonian regimes, characterizing Altland-Zirnbauer symmetries on 4-tori for multivariate quantum simulation and application to multiband superconductors, loop quantum gravity, and three-body and dissipative three-body dynamics.

I love to exercise, primarily in the form of resistance training. I play jazz and classical music on my electric bass and '00s-'20s indie rock on guitar. My other time is spent writing a book culminating information on over-the-counter dietary supplementation, with my perspective weighted by a deep passion for the accessibility of unbiased information for everyone. I  advocate for highly measured use of generative pre-trained and actively-trained learning models and work to encourage high scientific literacy by means of directed diligent study.