Analysis of Monte Carlo simulations of Quantum Spin Models

Poster Number

1

Lead Author Affiliation

Physics

Lead Author Status

Undergraduate - Senior

Faculty Mentor Name

Kieran Holland

Research or Creativity Area

Natural Sciences

Abstract

The purpose of this project is to examine various models of quantum magnetism, such as the Ising and Potts models, using Monte Carlo simulations to study how they are affected by temperature, and to apply various statistical techniques to analyze the numerical data from both. The Ising model, in particular, is used to study the phase transition of a ferromagnet at a certain temperature, where quantum spins are represented as points on a square lattice with two possible states, while the Potts model generalizes the spins to a larger number of states. Throughout this project I will describe the spin models I used and compare different possible Monte Carlo algorithms. I will also explain how I analyzed the data near the phase transition, using techniques such as Akaike Information Criterion and finite-size scaling.

Location

University of the Pacific, DeRosa University Center

Start Date

26-4-2025 10:00 AM

End Date

26-4-2025 1:00 PM

This document is currently not available here.

Share

COinS
 
Apr 26th, 10:00 AM Apr 26th, 1:00 PM

Analysis of Monte Carlo simulations of Quantum Spin Models

University of the Pacific, DeRosa University Center

The purpose of this project is to examine various models of quantum magnetism, such as the Ising and Potts models, using Monte Carlo simulations to study how they are affected by temperature, and to apply various statistical techniques to analyze the numerical data from both. The Ising model, in particular, is used to study the phase transition of a ferromagnet at a certain temperature, where quantum spins are represented as points on a square lattice with two possible states, while the Potts model generalizes the spins to a larger number of states. Throughout this project I will describe the spin models I used and compare different possible Monte Carlo algorithms. I will also explain how I analyzed the data near the phase transition, using techniques such as Akaike Information Criterion and finite-size scaling.