Speaker
Description
Disordered quantum spin chains are interesting systems as they can behave very differently from their clean counterparts. However simulating these systems pose signifciant challenges. First translation symmetry is broken when one considers a specific disorder realization, Secondly if one wants to study averaged observables it is necessary to average over many different disorder realization. In this talk I will present a tensor network method that addresses both issues by constructing a single, translationally-invariant Matrix Product Operator (MPO) which encodes the mixture of Gibbs states of the system in the thermodynamic limit. As a benchmark of the method we study the Infinite Randomness Fixed Point of the random transverse field Ising model.