Learning architectures based on quantum entanglement: a simple matrix product state algorithm for image recognition. - Yuhan Liu; Xiao Zhang; Maciej Lewenstein; Shi-Ju Ran (CoRR, 2018)

Generative Tensor Network Classification Model for Supervised Machine Learning. - Zheng-Zhi Sun; Cheng Peng; Ding Liu; Shi-Ju Ran; Gang Su (CoRR, 2019)

Quantum Compressed Sensing with Unsupervised Tensor Network Machine Learning. - Shi-Ju Ran; Zheng-Zhi Sun; Shao-Ming Fei; Gang Su; Maciej Lewenstein (CoRR, 2019)

Bayesian Tensor Network and Optimization Algorithm for Probabilistic Machine Learning. - Shi-Ju Ran (CoRR, 2019)

Tangent-Space Gradient Optimization of Tensor Network for Machine Learning. - Zheng-Zhi Sun; Shi-Ju Ran; Gang Su (CoRR, 2020)

Deep neural network predicts parameters of quantum many-body Hamiltonians by learning visualized wave-functions. - Xinran Ma; Z. C. Tu; Shi-Ju Ran (CoRR, 2020)

Residual Matrix Product State for Machine Learning. - Ye-Ming Meng; Jing Zhang; Peng Zhang; Chao Gao; Shi-Ju Ran (CoRR, 2020)

Automatically Differentiable Quantum Circuit for Many-qubit State Preparation. - Peng-Fei Zhou; Rui Hong; Shi-Ju Ran (CoRR, 2021)

Preparation of Many-body Ground States by Time Evolution with Variational Microscopic Magnetic Fields and Incomplete Interactions. - Ying Lu; Yue-Min Li; Peng-Fei Zhou; Shi-Ju Ran (CoRR, 2021)

Predicting Quantum Potentials by Deep Neural Network and Metropolis Sampling. - Rui Hong; Peng-Fei Zhou; Bin Xi; Jie Hu; An-Chun Ji; Shi-Ju Ran (CoRR, 2021)

Non-parametric Active Learning and Rate Reduction in Many-body Hilbert Space with Rescaled Logarithmic Fidelity. - Wei-Ming Li; Shi-Ju Ran (CoRR, 2021)

Entanglement-Based Feature Extraction by Tensor Network Machine Learning. - Yuhan Liu; Wen-Jun Li; Xiao Zhang; Maciej Lewenstein; Gang Su; Shi-Ju Ran (Frontiers Appl. Math. Stat., 2021)

Circuit encapsulation for efficient quantum computing based on controlled many-body dynamics. - Ying Lu; Peng-Fei Zhou; Shao-Ming Fei; Shi-Ju Ran (CoRR, 2022)

Unsupervised Recognition of Informative Features via Tensor Network Machine Learning and Quantum Entanglement Variations. - Sheng-Chen Bai; Yi-Cheng Tang; Shi-Ju Ran (CoRR, 2022)

Deep Machine Learning Reconstructing Lattice Topology with Strong Thermal Fluctuations. - Xiao-Han Wang; Pei Shi; Bin Xi; Jie Hu; Shi-Ju Ran (CoRR, 2022)

Intelligent diagnostic scheme for lung cancer screening with Raman spectra data by tensor network machine learning. - Yu-Jia An; Sheng-Chen Bai; Lin Cheng; Xiao-Guang Li; Cheng-en Wang; Xiao-Dong Han; Gang Su; Shi-Ju Ran; Cong Wang (CoRR, 2023)

Compressing neural network by tensor network with exponentially fewer variational parameters. - Yong Qing; Peng-Fei Zhou; Ke Li; Shi-Ju Ran (CoRR, 2023)

Persistent Ballistic Entanglement Spreading with Optimal Control in Quantum Spin Chains. - Ying Lu; Pei Shi; Xiao-Han Wang; Jie Hu; Shi-Ju Ran (CoRR, 2023)

Tensor networks for interpretable and efficient quantum-inspired machine learning. - Shi-Ju Ran; Gang Su (CoRR, 2023)

Many-body control with reinforcement learning and tensor networks. - Ying Lu; Shi-Ju Ran (Nat. Mac. Intell., 2023)

Universal replication of chaotic characteristics by classical and quantum machine learning. - Sheng-Chen Bai; Shi-Ju Ran (CoRR, 2024)

Universal scaling laws in quantum-probabilistic machine learning by tensor network towards interpreting representation and generalization powers. - Sheng-Chen Bai; Shi-Ju Ran (CoRR, 2024)