Tensor surgery and tensor rank
We introduce a method for transforming low-order tensors into higher-order tensors and apply it to tensors defined by graphs and hypergraphs. The transformation proceeds according to a surgery-like procedure that splits vertices, creates and absorbs virtual edges and inserts new vertices and edges. We show that tensor surgery is capable of preserving the low rank structure of an initial tensor decomposition and thus allows to prove nontrivial upper bounds on tensor rank, border rank and asymptotic rank of the final tensors. We illustrate our method with a number of examples. Tensor surgery on the triangle graph, which corresponds to the matrix multiplication tensor, leads to nontrivial rank upper bounds for all odd cycle graphs, which correspond to the tensors of iterated matrix multiplication. In the asymptotic setting we obtain upper bounds in terms of the matrix multiplication exponent $\omega$ and the rectangular matrix multiplication parameter $\alpha$. These bounds are optimal if $\omega$ equals two. We also give examples that illustrate that tensor surgery on general graphs might involve the absorption of virtual hyperedges and we provide an example of tensor surgery on a hypergraph. In the context of quantum information theory, our results may be interpreted as upper bounds on the number of Greenberger-Horne-Zeilinger states needed in order to create a network of Einstein-Podolsky-Rosen states by stochastic local operations and classical communication. They also imply new upper bounds on the nondeterministic quantum communication complexity of the equality problem when played among pairs of players.
Christandl, M, & Zuiddam, J. (2016). Tensor surgery and tensor rank.