Date of Award

2021

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Physics

First Advisor

Adrian Del Maestro

Abstract

For over thirty years, a long standing problem in quantum many-body physics has been to reliably extract dynamical information from imaginary time quantum Monte Carlo data. I report on a new method developed using modern evolutionary computation routines to approach this notoriously ill-posed problem. Motivation towards a solution will be presented as a brief summary of work on quantum simulations of low dimensional systems including helium on strained graphene and helium confined within rare gas plated mesoporous silica. The Differential Evolution for Analytic Continuation (DEAC) algorithm reconstructs the dynamic structure factor from imaginary time density-density correlations at zero and finite temperatures. Improved resolution of spectral features over earlier methods based on stochastic optimization and Bayesian inference is achieved. The need for fine-tuning of algorithmic control parameters is reduced by embedding them within the genome to be optimized. Benchmarks are presented for models where the dynamic structure factor is known.

Language

en

Number of Pages

217 p.

Available for download on Monday, March 14, 2022

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