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Publication Date
Spring 2018
Degree Type
Thesis - Campus Access Only
Degree Name
Master of Science (MS)
Department
Physics and Astronomy
Advisor
Ehsan Khatami
Keywords
condensed matter, Ising model, machine learning, neural network, quantum computer, spin glass
Subject Areas
Physics
Abstract
Growing interest in quantum computers and artificial intelligence has fueled several innovations in recent years with regards to machine learning. With the construction of the first experimental quantum annealers by D-Wave, attaining the significant speedup provided by a quantum computer seems within reach. However, recent tests bring into question the degree to which these quantum effects are being utilized. The presence of temperature chaos when solving for the ground-state energy of a spin-glass system and the unfavorable scaling with classical hardness suggest that the D-Wave may really be a semiclassical machine. Using a state-of-the-art parallel tempering code and by using artificial neural networks and t-distributed stochastic neighbor embedding, we search for features in spin-glass instances that cause temperature chaos, and by extension attempt to gain some insight into the performance and scaling problems of the D-Wave quantum computer. As a result, we found that temperature chaos must be caused by structures deeper than the interactions themselves.
Recommended Citation
Almada, Demetrius, "A Medley of Ising Models; Monte Carlo Solutions and Machine Learning Applications" (2018). Master's Theses. 4892.
DOI: https://doi.org/10.31979/etd.yh7f-39p9
https://scholarworks.sjsu.edu/etd_theses/4892