Many-Body Magic Via Pauli-Markov Chains—From Criticality to Gauge Theories by Poetri Sonya Tarabunga, Emanuele Tirrito, Titas Chanda, Marcello Dalmonte

“…We describe an efficient sampling procedure using tree tensor networks, that exploit their hierarchical structure leading to a modest O(log⁡N) computational scaling with system size. …”
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Learning nonequilibrium statistical mechanics and dynamical phase transitions by Ying Tang, Jing Liu, Jiang Zhang, Pan Zhang

“…While a number of methods have been proposed, such as tensor networks for one-dimensional lattices, we lack a method for arbitrary time beyond the steady state and for higher dimensions. …”
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From Non-Markovian Dissipation to Spatiotemporal Control of Quantum Nanodevices by Thibaut Lacroix, Brendon W. Lovett, Alex W. Chin

“…Exploiting numerically exact many body methods (tensor networks) we study a fully quantum model that allows us to explore how propagating environmental dynamics can instigate and direct the evolution of spatially remote, non-interacting quantum systems. …”
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Measurement-Induced Phase Transitions on Dynamical Quantum Trees by Xiaozhou Feng, Brian Skinner, Adam Nahum

“…Recent work made progress in the context of tree tensor networks, which can be related to all-to-all quantum circuit dynamics with forced (postselected) measurement outcomes. …”
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Optimal parameter configurations for sequential optimization of the variational quantum eigensolver by Katsuhiro Endo, Yuki Sato, Rudy Raymond, Kaito Wada, Naoki Yamamoto, Hiroshi C. Watanabe

“…Sequential optimization methods, which are often used in quantum circuit tensor networks, are popular for optimizing the parameterized gates of PQCs. …”
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Deterministic Constant-Depth Preparation of the AKLT State on a Quantum Processor Using Fusion Measurements by Kevin C. Smith, Eleanor Crane, Nathan Wiebe, S.M. Girvin

“…We elucidate our preparation scheme using the language of tensor networks and, furthermore, show that the Z_{2}×Z_{2} symmetry of the AKLT state directly affords this speed-up over previously known preparation methods. …”
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Local constants of motion imply information propagation by M Friesdorf, A H Werner, M Goihl, J Eisert, W Brown

“…We perform a detailed perturbation analysis of quasi-local constants of motion and also show that they indeed can be used to construct efficient spectral tensor networks, as recently suggested. Our results provide a detailed and at the same time model-independent picture of information propagation in MBL systems.…”
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MS-TR: A Morphologically enriched sentiment Treebank and recursive deep models for compositional semantics in Turkish by Sultan Zeybek, Ebubekir Koç, Aydın Seçer

“…Comparative results were obtained with the Recursive Neural Tensor Networks (RNTN) model which is operated over MS-TR, and conventional machine learning methods. …”
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Non-Parametric Semi-Supervised Learning in Many-Body Hilbert Space with Rescaled Logarithmic Fidelity by Wei-Ming Li, Shi-Ju Ran

“…The proposed QIKF and semi-supervised algorithm can be further combined with the parametric models such as tensor networks, quantum circuits, and quantum neural networks.…”
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Constrained HRT Surfaces and their Entropic Interpretation by Xi Dong, Donald Marolf, Pratik Rath

“…Our results are relevant to some of the issues associated with attempts to use standard random tensor networks to describe time dependent geometries.…”
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Geometric entanglement in topologically ordered states by Román Orús, Tzu-Chieh Wei, Oliver Buerschaper, Maarten Van den Nest

“…Additionally we relate our results to the behaviour of the relative entropy of entanglement in topologically ordered systems, and discuss a number of numerical approaches based on tensor networks that could be employed to extract this topological contribution for large systems beyond exactly solvable models.…”
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An Improved Bees Algorithm for Training Deep Recurrent Networks for Sentiment Classification by Sultan Zeybek, Duc Truong Pham, Ebubekir Koç, Aydın Seçer

“…The performance of the RNN model with BA-3+ has as good as for Tree-LSTMs and Recursive Neural Tensor Networks (RNTNs) language models, which achieved accuracy results of up to 90% for some datasets. …”
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