Search Results for author: Gang Su

Found 12 papers, 4 papers with code

Tensor networks for interpretable and efficient quantum-inspired machine learning

no code implementations19 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).

Tensor Networks

High-Efficient ab initio Bayesian Active Learning Method and Applications in Prediction of Two-dimensional Functional Materials

no code implementations22 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

Topological gimbal phonons in T-carbon

no code implementations13 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

Kagome quantum anomalous Hall effect with high Chern number and large band gap

no code implementations15 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

Tangent-Space Gradient Optimization of Tensor Network for Machine Learning

1 code implementation10 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.

BIG-bench Machine Learning

Quantum Compressed Sensing with Unsupervised Tensor-Network Machine Learning

no code implementations24 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.

BIG-bench Machine Learning

Generative Tensor Network Classification Model for Supervised Machine Learning

no code implementations26 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.

BIG-bench Machine Learning Classification +2

Quantum simulation for thermodynamics of infinite-size many-body systems by O(10) sites

1 code implementation3 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

Review of Tensor Network Contraction Approaches

1 code implementation30 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

Cannot find the paper you are looking for? You can Submit a new open access paper.