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.

本文展示了张量网络如何帮助发展可解释的机器学习算法，通过基于矩阵乘积态（MPS）的无监督聚类算法在对手生成的威胁情报实际应用中证明MPS在性能上能与传统的深度学习模型如自编码器和生成对抗网络相媲美，同时提供更丰富的模型可解释性，我 们的方法自然地促进了特征概率、Von Neumann熵和互信息的提取，为异常分类提供出色剧情并促进了无法理解人工智能决策背后的原理的前所未有的透明性和可解释性。

透明的机器学习在网络安全中的应用