Honors Theses
Date of Award
5-2-2019
Document Type
Undergraduate Thesis
Department
Physics and Astronomy
First Advisor
Kevin Beach
Relational Format
Dissertation/Thesis
Abstract
The application areas of machine learning techniques are becoming broader and increasingly ubiquitous in the natural sciences and engineering. One such field of interest within the physics community is the training and implementation of neural networks to aid in quantum many-body computations. Conversely, research exploring the possible computational benefits of using quantum many-body dynamics in the area of artificial intelligence and machine learning has also recently started to gain traction. The marriage of these fields comes naturally with the complementary nature of their mathematical frameworks. The objective of this study was to explore the possibility of encoding a quantum spin ½ system in a binary form in order to train a neural network. Once the spins are transformed into binary form, a bit count is calculated for each state of the system. An exact diagonalization of the XXZ Heisenberg Hamiltonian is then used to compute the energy eigenvectors and eigenvalues. The model is trained to identify the bit counts of the lowest energy state of the system which is found through a stochastic search algorithm known as simulated annealing.
Recommended Citation
Pompa, Daniel, "Encoding a 1-D Heisenberg Spin 1/2 Chain in a Simulated Annealing Algorithm for Machine Learning" (2019). Honors Theses. 1012.
https://egrove.olemiss.edu/hon_thesis/1012
Accessibility Status
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