2023/09/03 by Motohisa Fukuda, Fukuda, Motohisa · 1 citation
Computer Science · Mathematics · #Computational Physics (physics.comp-ph) #Computational Physics and Python Applications #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Theory (hep-th) #Machine Learning (cs.LG) #Mathematical Physics (math-ph) #Parallel Computing and Optimization Techniques #Strongly Correlated Electrons (cond-mat.str-el) #Tensor decomposition and applications
paper · pdf · doi:10.48550/arxiv.2309.01167
openalex publication_date 2023/09/03 · openalex created_date 2023/09/09 · openalex updated_date 2026/07/28
We are upgrading the Python-version of RTNI, which symbolically integrates tensor networks over the Haar-distributed unitary matrices. Now, PyRTNI2 can treat the Haar-distributed orthogonal matrices and the real and complex normal Gaussian tensors as well. Moreover, it can export tensor networks in the format of TensorNetwork so that one can make further calculations with concrete tensors, even for low dimensions, where the Weingarten functions differ from the ones for high dimensions. The tutorial notebooks are found at GitHub: https://github.com/MotohisaFukuda/PyRTNI2. In this paper, we explain maths behind the program and show what kind of tensor network calculations can be made with it. For the former, we interpret the element-wise moment calculus of the above random matrices and tensors in terms of tensor network diagrams, and argue that the view is natural, relating delta functions in the calculus to edges in tensor network diagrams.