176 citations · 1.4k across the 70 of their papers we have counts for
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Residual Matrix Product State for Machine Learning
Ye-Ming Meng, Jing Zhang, Peng Zhang +2
Tensor network, which originates from quantum physics, is emerging as an efficient tool for classical and quantum machine learning. Nevertheless, there still exists a considerable…
Deep learning Local Reduced Density Matrices for Many-body Hamiltonian Estimation
Xinran Ma, Z. C. Tu, Shi-Ju Ran
Human experts cannot efficiently access the physical information of quantum many-body states by simply "reading" the coefficients, but have to reply on the previous knowledge such…
Visual configuration segmentation of quantum states for phase identification in many-body systems
Yuan Yang, Zhengchuan Wang, Shi-Ju Ran +1
Artificial intelligence provides an unprecedented perspective for studying phases of matter in condensed-matter systems. Image segmentation is a basic technique of computer vision…
Visualizing Quantum Phases And Identifying Quantum Phase Transitions By Nonlinear Dimensionality Reduction
Yuan Yang, Zheng-Zhi Sun, Shi-Ju Ran +1
Identifying quantum phases and phase transitions is key to understand complex phenomena in statistical physics. In this work, we propose an unconventional strategy to access quantu…
Experimental realization of a quantum image classifier via tensor-network-based machine learning
Kunkun Wang, Lei Xiao, Wei Yi +2
Quantum machine learning aspires to overcome intractability that currently limits its applicability to practical problems. However, quantum machine learning itself is limited by lo…
Tangent-Space Gradient Optimization of Tensor Network for Machine Learning
Zheng-zhi Sun, Shi-ju Ran, Gang Su
The gradient-based optimization method for deep machine learning models suffers from gradient vanishing and exploding problems, particularly when the computational graph becomes de…