Date of Award
Spring 2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Chemistry
First Advisor
Batista, Victor
Abstract
This thesis introduces numerically exact methods for simulating the quantum dynamics of complex molecular systems beyond the limitations of current models like Transition State Theory and the Born-Oppenheimer approximation. By leveraging tensor-train-based solvers and advancements in quantum hardware, particularly bosonic circuit Quantum Electrodynamics (cQED) processors, we address the computational challenges posed by the "curse of dimensionality" of high-dimensional molecular wavepackets. Our methods demonstrate significant progress in modeling intricate chemical processes such as electron and energy transfer and photochemical reactions across various systems from biological complexes to in-solvent intramolecular charge transfer processes. We specifically focus on the development and application of the Tensor-Train Split-Operator KSL (TT-SOKSL) method for high-dimensional wavepacket dynamics and Tensor-Train Thermo-Field Dynamics (TT-TFD) method for high-dimensional density matrix propagation, meanwhile explore the feasibility of employing bosonic cQED devices for molecular simulations. This includes novel strategies like the single bosonic mode (SBM) mapping for cQED-based simulation of arbitrary molecular Hamiltonians and a holographic quantum computing scheme for Gaussian Boson Sampling tasks. Our findings not only offer new insights into quantum molecular dynamics but also pave the way for practical quantum computing applications in chemistry and drug discovery.
Recommended Citation
Lyu, Ningyi, "High-dimensional Molecular Quantum Dynamics with Tensor-Trains and Quantum Computation" (2024). Yale Graduate School of Arts and Sciences Dissertations. 1301.
https://elischolar.library.yale.edu/gsas_dissertations/1301
