72 citations · 240 across the 19 of their papers we have counts for
19 papers
Constructing 3D Rotational Invariance and Equivariance with Symmetric Tensor Networks
Meng Zhang, Chao Wang, Hao Zhang +2
Symmetry-aware architectures are central to geometric deep learning. We present a systematic approach for constructing continuous rotationally invariant and equivariant functions u…
Efficient learning of mixed-state tomography for photonic quantum walk
Qin-Qin Wang, Shaojun Dong, Xiao-Wei Li +7
Noise-enhanced applications in open quantum walk (QW) have recently seen a surge due to their ability to improve performance. However, verifying the success of open QW is challengi…
Beyond Boundaries: efficient Projected Entangled Pair States methods for periodic quantum systems
Shaojun Dong, Chao Wang, Hao Zhang +2
Projected Entangled Pair States (PEPS) are recognized as a potent tool for exploring two-dimensional quantum many-body systems. However, a significant challenge emerges when applyi…
Classical-Assisted Quantum Ground State Preparation with Tensor Network States and Monte Carlo Sampling
Feng-Yu Le, Zhao-Yun Chen, Lu Wang +7
Quantum computing offers potential solutions for finding ground states in condensed-matter physics and chemistry. However, achieving effective ground state preparation is also comp…
Learning Autonomous Vehicle Safety Concepts from Demonstrations
Karen Leung, Sushant Veer, Edward Schmerling +1
Evaluating the safety of an autonomous vehicle (AV) depends on the behavior of surrounding agents which can be heavily influenced by factors such as environmental context and infor…
A quantum spin liquid phase in the Kitaev-Hubbard model
Shaojun Dong, Hao Zhang, Chao Wang +3
The quantum spin liquid (QSL) state has been searched intensively in Kitaev-like materials, such as the Iridium oxides IrO and -RuCl. The half-filled Kitaev-Hubbard…
Entanglement entropy scaling of noisy random quantum circuits in two dimensions
Meng Zhang, Chao Wang, Shaojun Dong +3
Whether noisy quantum devices without error correction can provide quantum advantage over classical computers is a critical issue of current quantum computation. In this work, the…
Projected entangled pair states study of anisotropic-exchange magnets on triangular lattice
Meng Zhang, Chao Wang, Shaojun Dong +3
The anisotropic-exchange spin-1/2 model on a triangular lattice has been used to describe the rare-earth chalcogenides, which may have exotic ground states. We investigate the quan…
Simulation of the Ring-exchange Models with Projected Entangled Pair States
Chao Wang, Shaojun Dong, Yongjian Han +1
Algorithms to simulate the ring-exchange models using the projected entangled pair states (PEPS) are developed. We generalize the imaginary time evolution (ITE) method to optimize…
Hybrid convolutional neural network and PEPS wave functions for quantum many-particle states
Xiao Liang, Shao-Jun Dong, Lixin He
Neural networks have been used as variational wave functions for quantum many-particle problems. It has been shown that the correct sign structure is crucial to obtain the high acc…
Reply to comments on "Gapless spin liquid ground state of spin-1/2 - Heisenberg model on square lattices"
Wen-Yuan Liu, Shaojun Dong, Chao Wang +4
In a recent comments [arXiv:1909.12788 (2019)], Zhao et al. argue that the definition of dimer orders used in our paper [Phys. Rev. B 98, 241109 (2018)] may not rule out valence bo…
Stable diagonal stripes in the t-J model at =1/8 doping from fPEPS calculations
Shao-Jun Dong, Chao Wang, Yong-Jian Han +2
We investigate the 2D t-J model at a hole doping of =1/8 using recently developed high accuracy fermionic projected entangled pair states(fPEPS) method. By applying stoc…
Gradient optimization of fermionic projected entangled pair states on directed lattices
Shao-Jun Dong, Chao Wang, Yongjian Han +2
The recently developed stochastic gradient method combined with Monte Carlo sampling techniques [PRB {\bf 95}, 195154 (2017)] offers a low scaling and accurate method to optimize t…
Gapless spin liquid ground state of spin-1/2 - Heisenberg model on square lattices
Wen-Yuan Liu, Shaojun Dong, Chao Wang +4
The spin-1/2 - Heisenberg model on square lattices are investigated via the finite projected entangled pair states (PEPS) method. Using the recently developed gradient op…
PEPS++: Towards Extreme-scale Simulations of Strongly Correlated Quantum Many-particle Models on Sunway TainhuLight
Lixin He, Hong An, Chao Yang +10
The study of strongly frustrated magnetic systems has drawn great attentions from both theoretical and experimental physics. Efficient simulations of these models are essential for…
TNSPackage: A Fortran2003 library designed for tensor network state methods
Shao-Jun Dong, Wen-Yuan Liu, Chao Wang +3
Recently, the tensor network states (TNS) methods have proven to be very powerful tools to investigate the strongly correlated many-particle physics in one and two dimensions. The…
Gradient optimization of finite projected entangled pair states
Wen-Yuan Liu, Shao-Jun Dong, Yong-Jian Han +2
The projected entangled pair states (PEPS) methods have been proved to be powerful tools to solve the strongly correlated quantum many-body problems in two-dimension. However, due…
Tunneling frustration induced peculiar supersolid phases in the extended Bose-Hubbard model
Shao-Jun Dong, Wenyuan Liu, Xiang-Fa Zhou +4
By using a state of art tensor network state method, we study the ground-state phase diagram of an extended Bose-Hubbard model on the square lattice with frustrated next-nearest ne…
Efficient Scheme of Experimental Quantifying non-Markovianity in High-Dimension Systems
S. -J. Dong, B. -H. Liu, Y. -N. Sun +3
The non-Markovianity is a prominent concept of the dynamics of the open quantum systems, which is of fundamental importance in quantum mechanics and quantum information. Despite of…