Tensor Renormalization Group and its Application

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( n 1)M −ijkl1. Forward iterationM→M(0) (1)→L →M( N )( n)M i ' j ' kl ' '2. Backward iterationM→M( N) ( N−1)M(0)→L→ =MenvM M S S S S= ∑ ∑( n−1) ( n)ijkl i' j ' kl ' ' k' jp j ' pi i' lq l ' qki' j' k' l'pq
Accuracy of SRGD = 24Ising model on a triangular lattice
Page 1: Tensor Renormalization Group and it
Page 5 and 6: Characteristic Energy ScalesNumeric
Page 8 and 9: Numerical Renormalization Group Met
Page 10 and 11: Wilson block-spin NRGH 2keep the lo
Page 13 and 14: Density Matrix Renormalization Grou
Page 15 and 16: What is a “Density Matrix” meas
Page 17 and 18: Valence bond solid stateS=11 ⎡H =
Page 19 and 20: Hidden OrderHidden “antiferromagn
Page 21 and 22: Extend DMRG to 2D: Map 2D lattice t
Page 23 and 24: Why is the performance of DMRG poor
Page 25 and 26: Tensor-Network WavefunctionΨ= Tr
Page 27 and 28: How to determine the local tensor?[
Page 29 and 30: Projection Approachlimβ →∞e−
Page 31 and 32: Projection: x-bond∏−τH xi,je
Page 33 and 34: Expectation ValueΨ = Tr T [ m ] m
Page 35 and 36: Step I: RewiringM=∑kj,il mji mlkm
Page 37 and 38: Accuracy of TRGD = 24Ising model on
Page 39 and 40: I. Poor Man’s SRG: entanglement m
Page 41: II. More accurate treatment of SRGE
Page 45 and 46: Specific Heat of the Ising model on
Page 47 and 48: Heisenberg Model on Honeycomb Latti
Page 49 and 50: Staggered MagnetizationThe tensor-n
Page 51 and 52: Square Lattice
Page 53 and 54: Summary DMRG is an accurate numeric
Page 55 and 56: S 1S 2 S 3σ 1Tensor-network repres

Tensor Renormalization Group and its Application

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