Tensor Network Representation and Entanglement Spreading in Many-Body Localized Systems: A Novel Approach

• URL: http://arxiv.org/abs/2312.08170v1
• Date: Wed, 13 Dec 2023 14:28:45 GMT
• Title: Tensor Network Representation and Entanglement Spreading in Many-Body Localized Systems: A Novel Approach
• Authors: Z. Gholami, Z. Noorinejad, M. Amini, E. Ghanbari-Adivi
• Abstract summary: A novel method has been devised to compute the Local Integrals of Motion for a one-dimensional many-body localized system. A class of optimal unitary transformations is deduced in a tensor-network formalism to diagonalize the Hamiltonian of the specified system. The efficiency of the method was assessed and found to be both fast and almost accurate.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A novel method has been devised to compute the Local Integrals of Motion (LIOMs) for a one-dimensional many-body localized system. In this approach, a class of optimal unitary transformations is deduced in a tensor-network formalism to diagonalize the Hamiltonian of the specified system. To construct the tensor network, we utilize the eigenstates of the subsystems Hamiltonian to attain the desired unitary transformations. Subsequently, we optimize the eigenstates and acquire appropriate unitary localized operators that will represent the LIOMs tensor network. The efficiency of the method was assessed and found to be both fast and almost accurate. In framework of the introduced tensor-network representation, we examine how the entanglement spreads along the considered many-body localized system and evaluate the outcomes of the approximations employed in this approach. The important and interesting result is that in the proposed tensor network approximation, if the length of the blocks is greater than the length of localization, then the entropy growth will be linear in terms of the logarithmic time. Also, it has been demonstrated that, the entanglement can be calculated by only considering two blocks next to each other, if the Hamiltonian has been diagonalized using the unitary transformation made by the provided tensor-network representation.

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