In this paper we show how tensor networks help in developing explainability of machine learning algorithms. Specifically, we develop an unsupervised clustering algorithm based on Matrix Product States (MPS) and apply it in the context of a real use-case of adversary-generated threat intelligence. Our investigation proves that MPS rival traditional deep learning models such as autoencoders and GANs in terms of performance, while providing much richer model interpretability. Our approach naturally facilitates the extraction of feature-wise probabilities, Von Neumann Entropy, and mutual information, offering a compelling narrative for classification of anomalies and fostering an unprecedented level of transparency and interpretability, something fundamental to understand the rationale behind artificial intelligence decisions.

翻译：本文展示了张量网络如何助力提升机器学习算法的可解释性。具体而言，我们基于矩阵乘积态（MPS）开发了一种无监督聚类算法，并将其应用于实际场景中的对抗性威胁情报分析。研究表明，MPS在性能上与自动编码器、生成对抗网络（GANs）等传统深度学习模型相媲美，同时提供更丰富的模型可解释性。该方法天然支持提取特征概率、冯·诺依曼熵和互信息，为异常分类提供了有说服力的解释框架，并推动了前所未有的透明性与可解释性——这正是理解人工智能决策逻辑的关键基础。