The resource theory of tensor networks by Matthias Christandl, Vladimir Lysikov, Vincent Steffan, Albert H. Werner, Freek Witteveen

“…Tensor networks provide succinct representations of quantum many-body states and are an important computational tool for strongly correlated quantum systems. …”
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Sign Problem in Tensor-Network Contraction by Jielun Chen, Jiaqing Jiang, Dominik Hangleiter, Norbert Schuch

“…Using results from computational complexity, we observe that the approximate contraction of tensor networks with only positive entries has lower computational complexity as compared to tensor networks with general real or complex entries. …”
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On the computable cross norm in tensor networks and holography by Alexey Milekhin, Pratik Rath, Wayne Weng

“…We discuss the calculation of the CCNR in random tensor networks as well as holographic CFTs. The holographic dual involves a backreacted entanglement wedge cross section in a geometry sourced by Rényi-2 cosmic branes. …”
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Survey on Computational Applications of Tensor-Network Simulations by Marcos Diez Garcia, Antonio Marquez Romero

“…Tensor networks are a popular and computationally efficient approach to simulate general quantum systems on classical computers and, in a broader sense, a framework for dealing with high-dimensional numerical problems. …”
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Charting the space of ground states with tensor networks by Marvin Qi, David T. Stephen, Xueda Wen, Daniel Spiegel, Markus J. Pflaum, Agnès Beaudry, Michael Hermele

“…We employ matrix product states (MPS) and tensor networks to study topological properties of the space of ground states of gapped many-body systems. …”
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Generative learning of continuous data by tensor networks by Alex Meiburg, Jing Chen, Jacob Miller, Raphaëlle Tihon, Guillaume Rabusseau, Alejandro Perdomo-Ortiz

“…Beyond their origin in modeling many-body quantum systems, tensor networks have emerged as a promising class of models for solving machine learning problems, notably in unsupervised generative learning. …”
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Compressing Neural Networks Using Tensor Networks with Exponentially Fewer Variational Parameters by Yong Qing, Ke Li, Peng-Fei Zhou, Shi-Ju Ran

“…In this study, we propose a general compression scheme that considerably reduces the variational parameters of NNs, regardless of their specific types (linear, convolutional, etc.), by encoding them into deep automatically differentiable tensor networks (ADTNs) that contain exponentially fewer free parameters. …”
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Topology‐aware tensor decomposition for meta‐graph learning by Hansi Yang, Quanming Yao

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Federated learning with tensor networks: a quantum AI framework for healthcare by Amandeep Singh Bhatia, David E Bernal Neira

“…Currently, there are no known classical tensor networks (TNs) implemented in federated settings. …”
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Reflected entropy in random tensor networks. Part III. Triway cuts by Chris Akers, Thomas Faulkner, Simon Lin, Pratik Rath

“…Abstract For general random tensor network states at large bond dimension, we prove that the integer Rényi reflected entropies (away from phase transitions) are determined by minimal triway cuts through the network. …”
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Expectation-maximization alternating least squares for tensor network logistic regression by Naoya Yamauchi, Hidekata Hontani, Tatsuya Yokota, Tatsuya Yokota

“…In recent years, a learning method for classifiers using tensor networks (TNs) has attracted attention. When constructing a classification function for high-dimensional data using a basis function model, a huge number of basis functions and coefficients are generally required, but the TN model makes it possible to avoid the curse of dimensionality by representing the huge coefficients using TNs. …”
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Detecting memberships in multiplex networks via nonnegative matrix factorization and tensor decomposition by Fengqin Tang, Xiaozong Wang, Xuejing Zhao, Chunning Wang

“…Multiplex networks provide a powerful data structure for capturing diverse relationships among nodes, and the challenge of community detection within these networks has recently attracted considerable attention. …”
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A Spatio-Temporal Tensor Graph Neural Network-Based Method for Node-Link Prediction in Port Networks by Zhixin Xia, Zhangqi Zheng, Feiyang Wei, Yongshan Liu, Lu Yu

“…The updated spatial features and temporal edge embedding vectors are then weighted for feature fusion to predict the security of port network links. Finally, this paper conducts experiments on seven publicly available dynamic graph datasets, and the results show that the prediction accuracy of the spatio-temporal tensor graph neural network model is better than the baseline models, such as GC-LSTM, HTGN, and GAT, in general.…”
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Approximate Contraction of Arbitrary Tensor Networks with a Flexible and Efficient Density Matrix Algorithm by Linjian Ma, Matthew Fishman, Edwin Miles Stoudenmire, Edgar Solomonik

“…For contracting tensor networks defined on lattices, the proposed algorithm can be viewed as a generalization of the standard boundary-based algorithms. …”
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Tensor tree learns hidden relational structures in data to construct generative models by Kenji Harada, Tsuyoshi Okubo, Naoki Kawashima

“…Based on the tensor tree network with the Born machine framework, we propose a general method for constructing a generative model by expressing the target distribution function as the amplitude of the quantum wave function represented by a tensor tree. …”
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Tensor network finite-size scaling for two-dimensional 3-state clock model by Debasmita Maiti, Sing-Hong Chan, Pochung Chen

“…We benchmark recently proposed tensor network based finite-size scaling analysis in ( Phys. …”
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Multityped Community Discovery in Time-Evolving Heterogeneous Information Networks Based on Tensor Decomposition by Jibing Wu, Lianfei Yu, Qun Zhang, Peiteng Shi, Lihua Liu, Su Deng, Hongbin Huang

“…In this paper, we propose a multityped community discovery method for time-evolving heterogeneous information networks with general network schemas. A tensor decomposition framework, which integrates tensor CP factorization with a temporal evolution regularization term, is designed to model the multityped communities and address their evolution. …”
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KHNN: Hypercomplex-valued neural networks computations via Keras using TensorFlow and PyTorch by Agnieszka Niemczynowicz, Radosław A. Kycia

“…However, no general framework exists for constructing hypercomplex neural networks. …”
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Improving the prediction of bitumen’s density and thermal expansion by optimizing artificial neural networks with Optuna and TensorFlow by Eli I. Assaf, Xueyan Liu, Sandra Erkens

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White-Matter Connectivity and General Movements in Infants with Perinatal Brain Injury by Ellen N. Sutter, Jose Guerrero-Gonzalez, Cameron P. Casey, Douglas C. Dean, Andrea de Abreu e Gouvea, Colleen Peyton, Ryan M. McAdams, Bernadette T. Gillick

“…Tractography was used to identify the corticospinal tract, a key motor pathway often affected by perinatal brain injury, and tract-based spatial statistics (TBSS) were used to examine broader white matter networks. Diffusion parameters from the diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) models were compared between infants with and without typical general movements. …”
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