Publication
Explaining Anomalies with Tensor Networks
Hans Hohenfeld; Marius Beuerle; Elie Mounzer
In: 2025 IEEE International Conference on Quantum Artificial Intelligence. IEEE International Conference on Quantum Artificial Intelligence (IEEE QAI-2025), November 2-5, Neapel, Italy, IEEE, 11/2025.
Abstract
Tensor networks, a class of variational quantum
many-body wave functions have attracted considerable research
interest across many disciplines, including classical machine
learning. Recently, Aizpurua et al. demonstrated explainable
anomaly detection with matrix product states on a discrete-valued
cyber-security task, using quantum-inspired methods to gain
insight into the learned model and detected anomalies. Here, we
extend this framework to real-valued data domains. We furthermore introduce tree tensor networks for the task of explainable
anomaly detection. We demonstrate these methods with three
benchmark problems, show adequate predictive performance
compared to several baseline models and both tensor network
architectures’ ability to explain anomalous samples. We thereby
extend the application of tensor networks to a broader class of
potential problems and open a pathway for future extensions to
more complex tensor network architectures.