Efficient Contraction of Unstructured Tensor Networks

Tensor networks have gained significant interest recently as efficient means for numerically representing and manipulating quantum states and as a way to represent classical partition functions. Here we discuss a new method for numerically contracting unstructured finite local tensor networks. Finite size effects and multi-point correlations arise quite naturally from this approach, and we present results for a variety of physically interesting models which are difficult to analyze with other approaches.