Adaptive-weighted tree tensor networks for disordered quantum many-body systems

• URL: http://arxiv.org/abs/2111.12398v2
• Date: Mon, 6 Jun 2022 14:35:11 GMT
• Title: Adaptive-weighted tree tensor networks for disordered quantum many-body systems
• Authors: Giovanni Ferrari, Giuseppe Magnifico, Simone Montangero
• Abstract summary: We introduce an adaptive-weighted tree tensor network, for the study of disordered and inhomogeneous quantum many-body systems. We compute the ground state of the two-dimensional quantum Ising model in the presence of quenched random disorder and frustration.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce an adaptive-weighted tree tensor network, for the study of disordered and inhomogeneous quantum many-body systems. This ansatz is assembled on the basis of the random couplings of the physical system with a procedure that considers a tunable weight parameter to prevent completely unbalanced trees. Using this approach, we compute the ground state of the two-dimensional quantum Ising model in the presence of quenched random disorder and frustration, with lattice size up to $32 \times 32$. We compare the results with the ones obtained using the standard homogeneous tree tensor networks and the completely self-assembled tree tensor networks, demonstrating a clear improvement of numerical precision as a function of the weight parameter, especially for large system sizes.

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