Tensor Network

Tensor networks are mathematical structures used to represent and manipulate high-dimensional data efficiently, addressing the "curse of dimensionality" in various fields. Current research focuses on developing and applying tensor network architectures, such as tree tensor networks, tensor trains, and tensor rings, to diverse problems including machine learning (e.g., anomaly detection, regression, generative modeling), quantum physics simulations, and even industrial applications like web service QoS prediction and climate modeling. This approach offers advantages in terms of computational efficiency, model interpretability, and the ability to handle complex relationships within large datasets, impacting fields ranging from materials science to healthcare.