Theoretical Advancement of Xi Jinping’s Socialist Thought with Chinese Characteristics in the New Era under the Construction of Knowledge Map by Wang Lili

“…Then, the article proposes an entity alignment method based on Neural Tensor Network, which enhances the semantic and structural information utilization of entity embedding vectors through optimized NTN and joint knowledge representation learning. …”
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Emergence of Gapless Quantum Spin Liquid from Deconfined Quantum Critical Point by Wen-Yuan Liu, Juraj Hasik, Shou-Shu Gong, Didier Poilblanc, Wei-Qiang Chen, Zheng-Cheng Gu

“…Here, we show that a gapless QSL can naturally emerge from a DQCP. Via large-scale tensor network simulations of a square-lattice spin-1/2 frustrated Heisenberg model, both QSL-state and DQCP-type AFM-VBS transitions are observed. …”
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Chaos in quantum channels by Hosur, Pavan, Qi, Xiao-Liang, Yoshida, Beni, Roberts, Daniel Adam

“…We back up our results with numerics in two non-integrable models and analytic results in a perfect tensor network model of chaotic time evolution. These results show that the butterfly effect in quantum systems implies the information-theoretic definition of scrambling.…”
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Twice upon a time: timelike-separated quantum extremal surfaces by Engelhardt, Netta, Penington, Geoff, Shahbazi-Moghaddam, Arvin

“…Notably, our proposal suggests that the gravitational analogue of a tensor network is not necessarily the time-reflection symmetric slice, even when one exists.…”
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Variational quantum algorithm for unconstrained black box binary optimization: Application to feature selection by Christa Zoufal, Ryan V. Mishmash, Nitin Sharma, Niraj Kumar, Aashish Sheshadri, Amol Deshmukh, Noelle Ibrahim, Julien Gacon, Stefan Woerner

“…We apply our quantum-optimization-based feature selection algorithm, termed VarQFS, to build a predictive model for a credit risk data set with $20$ and $59$ input features (qubits) and train the model using quantum hardware and tensor-network-based numerical simulations, respectively. …”
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Efficient and feasible state tomography of quantum many-body systems by M Ohliger, V Nesme, J Eisert

“…Combining our approach with tensor network methods—in particular, the theory of matrix product states—we identify situations where the effort of reconstruction is even constant in the number of lattice sites, allowing, in principle, to perform tomography on large-scale systems readily available in present experiments.…”
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Scalable quantum tomography with fidelity estimation by Wang, Jun, Han, Zhao-Yu, Wang, Song-Bo, Li, Zeyang, Mu, Liang-Zhu, Fan, Heng, Wang, Lei

“…Keywords: Quantum tomography; Machine learning; Tensor network methods…”
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Simulating Large Quantum Circuits on a Small Quantum Computer by Peng, Tianyi, Harrow, Aram W, Ozols, Maris, Wu, Xiaodi

“…In this Letter, we introduce cluster parameters K and d of a quantum circuit. The tensor network of such a circuit can be decomposed into clusters of size at most d with at most K qubits of inter-cluster quantum communication. …”
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New developments in open quantum system dynamics by Lindoy, L

“…To overcome this difficulty, an efficient tree tensor network-based approach is presented and valided by treating a series of simple models. …”

Partons as unique ground states of quantum Hall parent Hamiltonians: The case of Fibonacci anyons by Mostafa Tanhayi Ahari, Sumanta Bandyopadhyay, Zohar Nussinov, Alexander Seidel, Gerardo Ortiz

“…Specifically, for parton-like states, we establish a link with tensor network structures of finite bond dimension that emerge via root level entanglement.…”
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An Analytic Method for Improving the Reliability of Models Based on a Histogram for Prediction of Companion Dogs’ Behaviors by Hye-Jin Lee, Sun-Young Ihm, So-Hyun Park, Young-Ho Park

“…A tightly coupled RGB time tensor network (TRT-Net) is proposed that minimizes the loss of spatiotemporal information by reflecting the three components (x-, y-, and z-axes) of the skeleton sequences in the corresponding three channels (red, green, and blue) for the behavioral classification of dogs. …”
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Fast simulation of planar Clifford circuits by David Gosset, Daniel Grier, Alex Kerzner, Luke Schaeffer

“…If it has a planar layout, then a tensor-network contraction algorithm due to Markov and Shi has a runtime exponential in the square root of its size, or more generally exponential in the treewidth of the underlying graph. …”
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Long-range topological insulators and weakened bulk-boundary correspondence by L Lepori, L Dell’Anna

“…Finally, we analyse the entanglement content of the paradigmatic LR Ising chain, inferring again important deviations from the SR regime, as well as the limitations of bulk-boundary (tensor-network based) approaches to classify LR spin models.…”
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Riemannian geometry and automatic differentiation for optimization problems of quantum physics and quantum technologies by Ilia A Luchnikov, Mikhail E Krechetov, Sergey N Filippov

“…Optimization with constraints is a typical problem in quantum physics and quantum information science that becomes especially challenging for high-dimensional systems and complex architectures like tensor networks. Here we use ideas of Riemannian geometry to perform optimization on the manifolds of unitary and isometric matrices as well as the cone of positive-definite matrices. …”
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Dynamical formation of a magnetic polaron in a two-dimensional quantum antiferromagnet by A Bohrdt, F Grusdt, M Knap

“…We provide an intuitive explanation of this dynamics in terms of a parton construction, which leads to a good quantitative agreement with the numerical tensor network state simulations. In the limit of infinite temperature and no spin exchange couplings, the dynamics can be approximated by a quantum random walk on a Bethe lattice with coordination number $\tilde {z}=4$ . …”
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Parallelizing Quantum Simulation With Decision Diagrams by Shaowen Li, Yusuke Kimura, Hiroyuki Sato, Masahiro Fujita

“…However, their advantage of memory efficiency does not let it replace the mainstream statevector and tensor network-based approaches. We argue the reason is the lack of effective parallelization strategies in performing calculations on DDs. …”
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Graph states as ground states of two-body frustration-free Hamiltonians by Andrew S Darmawan, Stephen D Bartlett

“…This  deformation can be applied to two-body frustration-free Affleck–Kennedy–Lieb–Tasaki (AKLT) models, yielding Hamiltonians that are exactly solvable with exact tensor network expressions for ground states. For the star-lattice AKLT model, the ground state of which is not expected to be a universal resource for MBQC, applying such a deformation appears to enhance the computational power of the ground state, promoting it to a universal resource for MBQC. …”
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Quantum Annealing in the NISQ Era: Railway Conflict Management by Krzysztof Domino, Mátyás Koniorczyk, Krzysztof Krawiec, Konrad Jałowiecki, Sebastian Deffner, Bartłomiej Gardas

“…As a reference, we also provide solutions calculated with classical methods, including the conventional solution of a linear integer version of the model as well as the solution of the QUBO model using a tensor network-based algorithm. Our preliminary results illustrate the degree of difficulty of real-life railway instances for the current quantum annealing technology. …”
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Unveiling new quantum phases in the Shastry-Sutherland compound SrCu2(BO3)2 up to the saturation magnetic field by T. Nomura, P. Corboz, A. Miyata, S. Zherlitsyn, Y. Ishii, Y. Kohama, Y. H. Matsuda, A. Ikeda, C. Zhong, H. Kageyama, F. Mila

“…Using ultrasound and magnetostriction techniques combined with extensive tensor-network calculations (iPEPS), several spin-supersolid phases are revealed between the 1/2 plateau and saturation (1/1 plateau). …”
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Spin supersolidity in nearly ideal easy-axis triangular quantum antiferromagnet Na2BaCo(PO4)2 by Yuan Gao, Yu-Chen Fan, Han Li, Fan Yang, Xu-Tao Zeng, Xian-Lei Sheng, Ruidan Zhong, Yang Qi, Yuan Wan, Wei Li

“…By combining the automatic parameter searching and tensor-network simulations, we establish a microscopic model description of this material with realistic model parameters, which can not only fit well the experimental thermodynamic data but also reproduce the measured magnetization curves without further adjustment of parameters. …”
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