Adaptive-weighted tree tensor networks for disordered quantum many-body systems
Giovanni Ferrari, Giuseppe Magnifico, Simone Montangero
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 $32\ifmmode\times\else\texttimes\fi{}32$. 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.