Interpreting machine learning of topological quantum phase transitions
Yi Zhang, Paul Ginsparg, Eun-Ah Kim
Abstract
The authors tackle the issue of interpretability in machine learning topological quantum phases in models of Chern insulator, ${\mathbb{Z}}_{2}$ topological insulator, and ${\mathbb{Z}}_{2}$ quantum spin liquid. The authors use artificial neural network aided by physical insight underlying the feature selection through quantum loop topography to understand the artificial neural network's decision-making criteria in each of the three cases
Topics & Concepts
InterpretabilityArtificial neural networkArtificial intelligenceQuantumFeature (linguistics)Topology (electrical circuits)Computer scienceQuantum computerPhysicsStability (learning theory)Feature selectionSpin (aerodynamics)Quantum phasesTopological orderMachine learningSelection (genetic algorithm)Theoretical physicsPhase (matter)Phase transitionQuantum phase transitionQuantum machine learningQuantum stateTopological data analysisQuantum informationClass (philosophy)Deep learningQuantum many-body systemsTopological Materials and PhenomenaMachine Learning in Materials Science