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

TL;DR

This paper presents an adaptive-weighted tree tensor network method for studying disordered quantum many-body systems, improving numerical precision over standard approaches, especially for large 2D systems with disorder.

Contribution

The paper introduces a novel adaptive-weighted tree tensor network ansatz tailored for disordered systems, enhancing accuracy in ground state computations.

Findings

Improved numerical precision with the adaptive-weighted approach.

Effective ground state computation for large 2D disordered systems.

Demonstrated superiority over standard and self-assembled tree tensor networks.

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.