Tensor network renormalization group study of spin-1 random Heisenberg chains
Zheng-Lin Tsai, Pochung Chen, Yu-Cheng Lin

TL;DR
This paper employs tensor network strong-disorder renormalization group techniques to analyze phase transitions and critical properties in disordered spin-1 Heisenberg chains, revealing new insights into the critical behavior and phase boundaries.
Contribution
It introduces an efficient tensor network SDRG method for studying disordered quantum chains and accurately characterizes the quantum critical point and phases in spin-1 chains.
Findings
Identified the quantum critical point between Haldane and random-singlet phases.
Determined critical exponents for string order and correlation functions.
Found improved agreement with theoretical predictions over previous studies.
Abstract
We use a tensor network strong-disorder renormalization group (tSDRG) method to study spin-1 random Heisenberg antiferromagnetic chains. The ground state of the clean spin-1 Heisenberg chain with uniform nearest-neighbor couplings is a gapped phase known as the Haldane phase. Here we consider disordered chains with random couplings, in which the Haldane gap closes in the strong disorder regime. As the randomness strength is increased further and exceeds a certain threshold, the random chain undergoes a phase transition to a critical random-singlet phase. The strong-disorder renormalization group method formulated in terms of a tree tensor network provides an efficient tool for exploring ground-state properties of disordered quantum many-body systems. Using this method we detect the quantum critical point between the gapless Haldane phase and the random-singlet phase via the…
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