46 citations · 88 across the 8 of their papers we have counts for
8 papers
Block Coordinate Descent Methods for Optimization under J-Orthogonality Constraints with Applications
Di He, Ganzhao Yuan, Xiao Wang +1
The J-orthogonal matrix, also referred to as the hyperbolic orthogonal matrix, is a class of special orthogonal matrix in hyperbolic space, notable for its advantageous properties.…
NG+ : A Multi-Step Matrix-Product Natural Gradient Method for Deep Learning
Minghan Yang, Dong Xu, Qiwen Cui +2
In this paper, a novel second-order method called NG+ is proposed. By following the rule ``the shape of the gradient equals the shape of the parameter", we define a generalized fis…
DST: Data Selection and joint Training for Learning with Noisy Labels
Yi Wei, Xue Mei, Xin Liu +1
Training a deep neural network heavily relies on a large amount of training data with accurate annotations. To alleviate this problem, various methods have been proposed to annotat…
Simulating noisy variational quantum eigensolver with local noise models
Jinfeng Zeng, Zipeng Wu, Chenfeng Cao +4
Variational quantum eigensolver (VQE) is promising to show quantum advantage on near-term noisy-intermediate-scale quantum (NISQ) computers. One central problem of VQE is the effec…
A variational quantum algorithm for Hamiltonian diagonalization
Jinfeng Zeng, Chenfeng Cao, Chao Zhang +2
Hamiltonian diagonalization is at the heart of understanding physical properties and practical applications of quantum systems. It is highly desired to design quantum algorithms th…
The impacts of optimization algorithm and basis size on the accuracy and efficiency of variational quantum eigensolver
Xian-Hu Zha, Chao Zhang, Dengdong Fan +4
Variational quantum eigensolver (VQE) is demonstrated to be the promising methodology for quantum chemistry based on near-term quantum devices. However, many problems are yet to be…
Sketchy Empirical Natural Gradient Methods for Deep Learning
Minghan Yang, Dong Xu, Zaiwen Wen +2
In this paper, we develop an efficient sketchy empirical natural gradient method (SENG) for large-scale deep learning problems. The empirical Fisher information matrix is usually l…
Simulating Noisy Quantum Circuits with Matrix Product Density Operators
Song Cheng, Chenfeng Cao, Chao Zhang +4
Simulating quantum circuits with classical computers requires resources growing exponentially in terms of system size. Real quantum computer with noise, however, may be simulated p…