A Medley of Ising Models; Monte Carlo Solutions and Machine Learning Applications

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