Title: Foundations and Computation of Adaptive Tensor Networks

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates the theoretical basis and computational applications of adaptive tensor networks. The core claim is to establish a theoretical framework that dynamically adjusts the structure of tensor networks based on input data and computational process, enabling more efficient matrix operations and numerical analysis. This is motivated by the limitations of current methods in handling complex calculations and offers a potential solution to computational bottlenecks. The paper details a novel algorithm which employs tensor network properties to calculate and dynamically restructure the tensor network, achieving enhanced performance through automated adjustment. The research explores the potential of this approach to address the challenges associated with traditional matrix multiplication and numerical methods. We present a detailed description of the proposed algorithm, its theoretical underpinnings, and preliminary results demonstrating its effectiveness. Finally, we discuss the implications of this work for the future of computational science and engineering.

DOI: 10.5281/zenodo.22192499

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