Entanglement features of random neural network quantum states
Xiao-Qi Sun, Tamra Nebabu, Xizhi Han, Michael O. Flynn, Xiao-Liang Qi
Abstract
Neural networks offer a novel approach to represent wave functions for solving quantum many-body problems. But what kinds of quantum states are efficiently represented by neural networks? To approach this problem, the authors study here entanglement properties of an ensemble of neural network states represented by random restricted Boltzmann machines. Phases with distinct entanglement features are identified and characterized. In particular, the authors show that these neural network states can look typical in their entanglement profile while still being distinguishable from a typical state by their fractal dimensions.
Topics & Concepts
Quantum entanglementPhysicsQuantum mechanicsStatistical physicsQuantumArtificial neural networkQuantum discordQuantum networkComputer scienceArtificial intelligenceQuantum many-body systemsNeural Networks and Reservoir ComputingModel Reduction and Neural Networks