Speaker
Description
Tensor network methods and quantum information science have enriched each other over the past few decades. In this talk, I will present two examples of this interplay from our recent works. First, in the direction of “tensor networks for quantum,” I will discuss the complexity of classically simulating a two-dimensional quantum sampling architecture in the presence of disorder. Using exact tensor-network contractions to evaluate output probabilities, we show that disorder in two-qubit interactions induces two crossovers, each undermining one of the two conjectured ingredients underlying sampling hardness. In the opposite direction, “quantum for tensor networks,” we develop a new algorithm for disentangling tensor trains using a restricted set of two-qubit Clifford gates. These disentangling transformations enable more efficient tensor cross interpolation at essentially no additional computational cost.