Symbolically integrating tensor networks over various random tensors by the second version of Python RTNI

TL;DR

The paper presents an upgraded Python tool, PyRTNI2, that symbolically integrates tensor networks over various random matrices and tensors, enabling more versatile calculations and exports for low-dimensional cases.

Contribution

PyRTNI2 extends the original RTNI to handle orthogonal matrices and Gaussian tensors, and supports exporting tensor networks for further concrete tensor calculations.

Findings

Supports integration over orthogonal matrices and Gaussian tensors.

Exports tensor networks compatible with TensorNetwork for further analysis.

Enables calculations for low-dimensional tensor networks with different Weingarten functions.

Abstract

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.