{"ID":2840669,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.13514","arxiv_id":"2511.13514","title":"A Quantum Tensor Network-Based Viewpoint for Modeling and Analysis of Time Series Data","abstract":"Accurate uncertainty quantification is a critical challenge in machine learning. While neural networks are highly versatile and capable of learning complex patterns, they often lack interpretability due to their ``black box'' nature. On the other hand, probabilistic ``white box'' models, though interpretable, often suffer from a significant performance gap when compared to neural networks. To address this, we propose a novel quantum physics-based ``white box'' method that offers both accurate uncertainty quantification and enhanced interpretability. By mapping the kernel mean embedding (KME) of a time series data vector to a reproducing kernel Hilbert space (RKHS), we construct a tensor network-inspired 1D spin chain Hamiltonian, with the KME as one of its eigen-functions or eigen-modes. We then solve the associated Schr{ö}dinger equation and apply perturbation theory to quantify uncertainty, thereby improving the interpretability of tasks performed with the quantum tensor network-based model. We demonstrate the effectiveness of this methodology, compared to state-of-the-art ``white box\" models, in change point detection and time series clustering, providing insights into the uncertainties associated with decision-making throughout the process.","short_abstract":"Accurate uncertainty quantification is a critical challenge in machine learning. While neural networks are highly versatile and capable of learning complex patterns, they often lack interpretability due to their ``black box'' nature. On the other hand, probabilistic ``white box'' models, though interpretable, often suf...","url_abs":"https://arxiv.org/abs/2511.13514","url_pdf":"https://arxiv.org/pdf/2511.13514v1","authors":"[\"Pragatheeswaran Vipulananthan\",\"Kamal Premaratne\",\"Dilip Sarkar\",\"Manohar N. Murthi\"]","published":"2025-11-17T15:49:17Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.IT\"]","methods":"[]","has_code":false}