  We propose a tensor network method for investigating strongly disordered
systems that is based on an adaptation of entanglement renormalization [G.
Vidal, Phys. Rev. Lett. 99, 220405 (2007)]. This method makes use of the strong
disorder renormalization group to determine the order in which lattice sites
are coarse-grained, which sets the overall structure of the corresponding
tensor network ansatz, before optimization using variational energy
minimization. Benchmark results from the disordered XXZ model demonstrates that
this approach accurately captures ground state entanglement in disordered
systems, even at long distances. This approach leads to a new class of
efficiently contractible tensor network ansatz for 1D systems, which may be
understood as a generalization of the multi-scale entanglement renormalization
ansatz for disordered systems.
