Neural-network quantum states at finite temperature
Naoki Irikura, Hiroki Saito
Abstract
The authors propose a variational method to obtain the finite temperature density matrix of a quantum many body system using a convolutional neural network with machine learning technique. This method is applied to the Bose-Hubbard model.
Topics & Concepts
QuantumArtificial neural networkConvolutional neural networkHubbard modelDensity matrixComputer sciencePhysicsMatrix (chemical analysis)Quantum mechanicsStatistical physicsArtificial intelligenceMaterials scienceComposite materialSuperconductivityQuantum many-body systemsCold Atom Physics and Bose-Einstein CondensatesQuantum, superfluid, helium dynamics