Electrical and Computer Engineering ETDs
Publication Date
Spring 5-17-2025
Abstract
In this thesis we study the quantum many-body problem with tools from machine learning. In particular, we utilize Neural Quantum States (NQS) as an ansatz in Variational Monte Carlo (VMC) simulations. First, we develop a method to train NQS more reliably in an effort to reduce the total computational cost of finding the ground state of strongly-correlated systems. Then, we develop an expressive NQS ansatz to study two-dimensional materials and perform ab-initio phase discovery. In particular, we consider the two-dimensional electron gas, and provide state-of-the-art energies and characterization of the phase diagram. We also find evidence of a novel state between the Fermi liquid and Wigner crystal which we call a Nematic Spin Correlated Liquid (NSCL). Lastly, we turn our attention to a moir\'e system with a honeycomb potential. With minor modifications to the ansatz, we find a highly correlated state we call an interaction-driven molecular Wigner crystal. With the increasing interest in NQS as an accurate method to study the many-body problem and recent advances in experimentally studying two-dimensional materials, we expect the technical developments and physical results presented in this thesis to be valuable to theorists and experimentalists.
Keywords
Quantum Physics, Neural Quantum States, Two-Dimensional Electron Gas, Moire, Neural Networks, Machine Learning
Document Type
Dissertation
Language
English
Degree Name
Computer Engineering
Level of Degree
Doctoral
Department Name
Electrical and Computer Engineering
First Committee Member (Chair)
Milad Marvian
Second Committee Member
Manel Martinez-Ramon
Third Committee Member
Susan R. Atlas
Fourth Committee Member
Tameem Albash
Recommended Citation
Smith, Conor. "Ab-Initio Phase Discovery with Neural Quantum States." (2025). https://digitalrepository.unm.edu/ece_etds/709
