Title: Neuromorphic Tensor Network Analysis (NTTA)

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper introduces Neuromorphic Tensor Network Analysis (NTTA), a novel method for analyzing tensor networks leveraging neural networks. Traditional approaches to tensor network analysis are computationally demanding, limiting their applicability to complex network structures. NTTA employs a trainable neural network to directly predict network connectivity and properties, offering a significant acceleration and efficiency improvement. The core mechanism centers on learning a representation of the tensor network structure, enabling rapid assessment of network properties without the need for exhaustive manual analysis. We present a framework for training and evaluating this network, demonstrating its effectiveness in analyzing diverse tensor network configurations. The resulting system facilitates the computationally efficient exploration of tensor network properties, potentially accelerating research and applications across various fields, including machine learning, materials science, and neuroscience.

DOI: 10.5281/zenodo.22272813

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