发表机构
Dipartimento Interuniversitario di Fisica, Università degli Studi di Bari Aldo Moro; Istituto Nazionale di Fisica Nucleare, Sezione di Bari; Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Università degli Studi di Bari Aldo Moro; Dipartimento Di Scienze Del Suolo, Della Pianta e Degli Alimenti (DISSPA), Università degli Studi di Bari Aldo Moro（巴里阿尔多·莫罗大学物理跨大学部门; 巴里国家核物理研究所; 巴里阿尔多·莫罗大学生物医学转化与神经科学部门; 巴里阿尔多·莫罗大学土壤、植物和食品科学部门）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
介绍受量子启发的张量网络框架用于加尔加诺地区野火易感性分类，结合地理空间表示与量子掩码，可二分类和多分类。揭示二分类学习转变，分析多分类混淆，引入诊断表明模型能编码类可区分性层次结构，兼具精度与解释性。
AI 中文摘要
本文介绍了一种受量子启发的张量网络框架，用于加尔加诺地区的野火易感性分类，利用了AlphaEarth嵌入和矩阵乘积态模型。该方法将可扩展的地理空间表示与可解释的量子掩码相结合，实现了野火易感性的二分类和多分类。研究揭示了二分类情况下明显的学习转变，并详细分析了多分类设置中的类间混淆。通过引入基于约化密度矩阵的层级分辨混合性诊断，表明MPS分类器自然地编码了类可区分性的层次结构。这些结果表明张量网络模型不仅能实现有竞争力的分类精度，还提供了一个基于物理的框架来量化和解释复杂环境数据集中的类可分离性。
英文摘要
A quantum-inspired tensor network framework for wildfire susceptibility classification in the Gargano region is introduced, leveraging AlphaEarth embeddings and Matrix Product State models. The approach combines scalable geospatial representations with an interpretable quantum mask, enabling both binary and multiclass classification of wildfire susceptibility. Beyond predictive performance, the study reveals a pronounced grokking transition in the binary case and provides a detailed analysis of inter-class confusion in the multiclass setting. By introducing level-resolved mixedness diagnostics based on reduced density matrices, we show that the MPS classifier naturally encodes a hierarchy of class distinguishability, with non-adjacent categories becoming more separable than neighboring ones. These results demonstrate that tensor network models not only achieve competitive classification accuracy but also offer a physically grounded framework to quantify and interpret class separability in complex environmental datasets.
