Entanglement-based tensor-network strong-disorder renormalization group

Abstract

We propose an entanglement-based algorithm of the tensor-network strong-disorder renormalization group (tSDRG) method for quantum spin systems with quenched randomness. In contrast to the previous tSDRG algorithm based on the energy spectrum of renormalized block Hamiltonians, we directly utilizes the entanglement structure associated with the blocks to be renormalized. We examine accuracy of the new algorithm for the random antiferromagnetic Heisenberg models on the one-dimensional, triangular, and square lattices. We then find that the entanglement-based tSDRG achieves better accuracy than the previous one for the square lattice model with weak randomness, while it is less efficient for the one-dimensional and triangular lattice models particularly in the strong randomness region. The theoretical background and possible improvements of the algorithm are also discussed.

arXiv commentv2: 11 pages, 8 figures, 2 tables, accepted version

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Cited by in corpus (3)

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• Adaptive-weighted tree tensor networks for disordered quantum many-body systems
• Entanglement bipartitioning and tree tensor networks