Speaker
Description
Tensor network approach provides us with a novel framework to study the field theories on a lattice without resorting to the probabilistic interpretation. Therefore, the tensor network approach is expected to be free from the sign problem. On the other hand, we have to introduce a reliable regularization scheme to discretize the continuous degrees of freedom, otherwise we cannot perform tensor network computations. In this study, we introduce the spectral clustering technique, a non-linear dimensional reduction scheme commonly employed in machine learning, to initially compress tensor network representations before applying tensor network algorithms. We discuss its efficacy in tensor renormalization group calculations on the SU(2) and SU(3) Yang-Mills theories on a square lattice.
