Overview

Tensor networks study how to represent large arrays, probability distributions, quantum states, and neural-network parameters as networks of smaller tensors linked by bond indices. Common forms such as matrix product states, tree tensor networks, and matrix product operators support compression, inference, scientific computing, and quantum-circuit simulation by exploiting low-rank structure. Researchers develop rank-selection and contraction algorithms alongside learning methods, including Riemannian optimization, tensorized neural networks, and hardware-aware implementations. A central tension is that compact representations can reduce memory and computation, but their accuracy and practical speed depend on the data’s structure, the chosen network geometry, and the costs of training and contraction.

Latest papers

CardsList

50 papers5 papers in the last four weeks, down 29% on the four weeks before. 0.0% of all new papers.

  1. Tensor Network Datamodels for Training Data Attribution

    Oct 17, 2026Farzaneh Heidari, Md Rifat Arefin, Louis Jaburi +1Training Data AttributionTensor Networks

  2. Activation-Aware Weight Tensorization: A Calibration-Time Preconditioner for Tensor-Network LLM Compression

    Oct 7, 2026Alessandro Beatini, Marco Maronese, Emanuele RodolàLLM CompressionTensor Networks

  3. Gaussian Universality and Its Breakdown in Tensor-Network Machine Learning

    Oct 5, 2026Shi-Tuan Wang, Zidu Liu, Li-Wei YuTensor NetworksGaussian Processes

  4. Prototype-Rule Neurosymbolic Regularization for Rank-Constrained Tensor Neural Networks under Label Scarcity

    Sep 30, 2026Eftychios Protopapadakis, Konstantinos Makantasis, Konstantinos M. GiannoutakisTensor NetworksPrototype Learning

  5. On Probabilistic Inference Through Parametric Tensor Decomposition in Base Tensor Networks

    Sep 20, 2026Sagad Hamid, Tanya BraunEfficient InferenceProbabilistic Graphical Models

  6. Quantum-Inspired Trainable and Parameter-Efficient Tensor Networks for Image Inpainting

    Sep 15, 2026Shiwen An, Konstantinos SlavakisTensor NetworksQuantum-Inspired ML

  7. Benign Loss Landscapes Can Coexist with Worst-Case Hardness

    Sep 11, 2026Zach Furman, Stephan Wäldchen, Yangda Bei +1Tensor NetworksStatistical Learning Theory

  8. Tensor Network Moral Graph Recovery of Discrete Probability Distributions

    Sep 8, 2026Á. Troyano Olivas, Chi-Hang Fred Fung, Hans H. Brunner +2Tensor NetworksGraph Structure Learning

  9. Tensor network representations of discrete maximum entropy distributions via mean polytopes

    Sep 7, 2026Alex Goessmann, Martin EigelTensor NetworksExponential Family

  10. Parameterised graph theory for tensor networks: entanglement rerouting, structural simplification, and agnostic tomography

    Sep 3, 2026Matthias C. Caro, Natalie McHugh, Sergii StrelchukTensor NetworksQuantum State Tomography

  11. Stochastic Optimization of Tree Tensor Networks

    Sep 1, 2026Marius Willner, Maximilian Scharf, André Uschmajew +2Tensor NetworksRiemannian Optimization

  12. Quantum Tensor Network Learning with DMRG

    Aug 19, 2026Gustav J L Jäger, Martin B Plenio, Hans-Martin RieserTensor NetworksQuantum Machine Learning

  13. Iterative tensor network transformations for element-wise evaluation of elementary and filtering functions

    Aug 17, 2026Xiao Wang, Tomohiro Hashizume, Pia Siegl +1Tensor Networks

  14. Hamilton-Zero: A Neural Tensor-Network Foundation Model for Ground States of Arbitrary Quadratic Qubit Hamiltonians

    Aug 12, 2026Timothy Heightman, Elena Orlova, Philip Mantrov +1Tensor NetworksNeural Quantum States

  15. sLTN: Structural Logic Tensor Networks

    Aug 11, 2026Davide Rinaldi, Luciano SerafiniTensor NetworksLogic Tensor Networks

  16. Hierarchical rank-evolving representation for physics-informed neural networks

    Aug 10, 2026Ruoyang Su, Xi-Le Zhao, Kun Li +1Tensor NetworksNeural PDE Solvers

  17. Tensor Network Kernel Machines: A JAX Framework for Machine Learning and Nonlinear System Identification

    Aug 7, 2026Albert Saiapin, Kim BatselierTensor NetworksNonlinear System Identification

  18. Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation

    Aug 6, 2026Alfred M. Pastor, Maribel Castillo, Jose M. BadiaTensor NetworksLearning to Rank

  19. Tensor Network Machine Learning for Wildfire Susceptibility Mapping: from Grokking Dynamics to Quantum Mixedness of Class Representations

    Jul 21, 2026Domenico Pomarico, Alessandra Costantino, Gabriel Ramirez Sanchez +12Tensor NetworksNeural Network Interpretability

  20. Entanglement geometry separates circuit cutting, classical hardness, and trainability

    Jul 20, 2026Maria Gragera Garces, Sabina Drăgoi, Lirandë PiraTensor NetworksQuantum Circuit Simulation

  21. (MPO)2^2: Multivariate Polynomial Optimization based on Matrix Product Operators

    Jul 17, 2026Niccolò Ciolli, Anders Vestergaard Nørskov, Michael Kastoryano +2Tensor NetworksTensor Decomposition

  22. Multi-agent Autoformalization of Tensor Network Theory

    Jul 8, 2026Sirui Lu, Erickson Tjoa, J. Ignacio CiracTensor NetworksMulti-Agent LLM Systems

  23. Entropy-Guided Tensor Compression for Multimodal Federated Learning on Edge Devices

    Jul 7, 2026Quoc Bao Phan, Tuy Tan NguyenTensor NetworksMultimodal Federated Learning

  24. First-Order Temporal Logic Tensor Networks

    Jun 29, 2026Luca Boscarato, Ivan Donadello, Alessandro Artale +2Tensor NetworksLogic Tensor Networks

  25. fTNN: a tensor neural network for fractional PDEs

    Jun 25, 2026Qingkui Ma, Hehu Xie, Xiaobo YinTensor NetworksNeural PDE Solvers

  26. Stochastic trace estimation with tensor train random vectors

    Jun 14, 2026Zvonimir Bujanović, Daniel Kressner, Hrvoje OlićTensor NetworksRandomized Sketching

  27. Family-Aware Residual Architecture for Predicting Quantum Circuit Simulation Performance

    Jun 10, 2026Honjar Xing, Yehong Jiang, Xianbang Wang +2Tensor NetworksQuantum Circuit Simulation

  28. EinSort: Sorting is All We Need for Tensorizing LLM

    Jun 7, 2026Toshiaki Koike-Akino, Jing Liu, Ye WangTensor NetworksLLM Compression

  29. Algorithmic algorithm development with LLMs: A Case Study on LLM-Usage for Contraction Order Optimization in Tensor Networks

    Jun 1, 2026Fabian Hoppe, Melven Röhrig-Zöllner, Philipp KnechtgesTensor NetworksAutomated Algorithm Discovery