no code implementations • 19 Nov 2023 • Shi-Ju Ran, Gang Su
It is a critical challenge to simultaneously gain high interpretability and efficiency with the current schemes of deep machine learning (ML).
no code implementations • 11 Mar 2023 • Yu-Jia An, Sheng-Chen Bai, Lin Cheng, Xiao-Guang Li, Cheng-en Wang, Xiao-Dong Han, Gang Su, Shi-Ju Ran, Cong Wang
The accuracy of the samples with high certainty is almost 100$\%$.
no code implementations • 22 Feb 2021 • Xing-Yu Ma, Hou-Yi Lyu, Kuan-Rong Hao, Zhen-Gang Zhu, Qing-Bo Yan, Gang Su
Beyond the conventional trial-and-error method, machine learning offers a great opportunity to accelerate the discovery of functional materials, but still often suffers from difficulties such as limited materials data and unbalanced distribution of target property.
Active Learning Materials Science
no code implementations • 13 Jan 2021 • Jing-Yang You, Xian-Lei Sheng, Gang Su
At about 15. 2 THz, we find that there exist three mutually intersecting nodal loops (named as nodal gimbal phonons) around {\Gamma} point, and two pairs of type-I Weyl phonons on the boundary of Brillouin zone around each X point.
Materials Science
no code implementations • 15 Oct 2020 • Zhen Zhang, Jing-Yang You, Xing-Yu Ma, Bo Gu, Gang Su
For the bilayer compound Co6Sn5Se4, it becomes a half-metal, with a relatively flat plateau in its anomalous Hall conductivity corresponding to |C| = 3 near the Fermi level.
Materials Science
1 code implementation • 10 Jan 2020 • 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 deep.
no code implementations • 24 Jul 2019 • Shi-Ju Ran, Zheng-Zhi Sun, Shao-Ming Fei, Gang Su, Maciej Lewenstein
To transfer a specific piece of information with $|\Psi \rangle$, our proposal is to encode such information in the separable state with the minimal distance to the measured state $|\Phi \rangle$ that is obtained by partially measuring on $|\Psi \rangle$ in a designed way.
no code implementations • 26 Mar 2019 • Zheng-Zhi Sun, Cheng Peng, Ding Liu, Shi-Ju Ran, Gang Su
By investigating the distances in the many-body Hilbert space, we find that (a) the samples are naturally clustering in such a space; and (b) bounding the bond dimensions of the TN's to finite values corresponds to removing redundant information in the image recognition.
1 code implementation • 3 Oct 2018 • Shi-Ju Ran, Bin Xi, Cheng Peng, Gang Su, Maciej Lewenstein
In this work we propose to simulate many-body thermodynamics of infinite-size quantum lattice models in one, two, and three dimensions, in terms of few-body models of only O(10) sites, which we coin as quantum entanglement simulators (QES's).
Strongly Correlated Electrons Computational Physics Quantum Physics
no code implementations • ICLR 2018 • Ding Liu, Shi-Ju Ran, Peter Wittek, Cheng Peng, Raul Blázquez García, Gang Su, Maciej Lewenstein
The resemblance between the methods used in studying quantum-many body physics and in machine learning has drawn considerable attention.
3 code implementations • ICLR 2018 • Ding Liu, Shi-Ju Ran, Peter Wittek, Cheng Peng, Raul Blázquez García, Gang Su, Maciej Lewenstein
We study the quantum features of the TN states, including quantum entanglement and fidelity.
1 code implementation • 30 Aug 2017 • Shi-Ju Ran, Emanuele Tirrito, Cheng Peng, Xi Chen, Gang Su, Maciej Lewenstein
One goal is to provide a systematic introduction of TN contraction algorithms (motivations, implementations, relations, implications, etc.
Computational Physics Statistical Mechanics Strongly Correlated Electrons Applied Physics Quantum Physics