Identifying Topological Phase Transitions in Experiments Using Manifold Learning
Eran Lustig, Or Yair, Ronen Talmon, Mordechai Segev
Abstract
We demonstrate the identification of topological phase transitions from experimental data using diffusion maps: a nonlocal unsupervised machine learning method. We analyze experimental data from an optical system undergoing a topological phase transition and demonstrate the ability of this approach to identify topological phase transitions even when the data originates from a small part of the system, and does not even include edge states.
Topics & Concepts
Phase transitionManifold (fluid mechanics)Topological data analysisTopological orderTopology (electrical circuits)Diffusion mapPhase (matter)Enhanced Data Rates for GSM EvolutionStatistical physicsPhysicsIdentification (biology)Nonlinear dimensionality reductionComputer scienceArtificial intelligenceAlgorithmCondensed matter physicsQuantum mechanicsMathematicsDimensionality reductionBiologyQuantumCombinatoricsMechanical engineeringEngineeringBotanyTopological Materials and PhenomenaQuantum many-body systemsTopological and Geometric Data Analysis