1.1k citations
- China Academy of Engineering PhysicsCN53 papers
- Beijing Computational Science Research CenterCN13 papers
- Chinese Academy of SciencesCN6 papers
- Beijing Institute of TechnologyCN5 papers
- Peking UniversityCN5 papers
- Fudan UniversityCN4 papers
- Institute of Theoretical PhysicsCN4 papers
- State Key Laboratory of Surface Physics4 papers
- Sun Yat-sen UniversityCN4 papers
- The Dodd-Walls Centre for Photonic and Quantum TechnologiesNZ4 papers
- University of OtagoNZ4 papers
- Zhejiang UniversityCN4 papers
53 papers
Diffusive Pseudo-Conformal Mapping: Anisotropy-Free Transformation Thermal Media with Perfect Interface Matching
Gaole Dai, Fubao Yang, Jun Wang +2
Transformation media provide a fundamental paradigm for field regulation, but their tricky anisotropy challenges fabrication. Though optical conformal mapping has been utilized to…
Measurement-efficient quantum Krylov subspace diagonalisation
Zongkang Zhang, Anbang Wang, Xiaosi Xu +1
The Krylov subspace methods, being one category of the most important classical numerical methods for linear algebra problems, can be much more powerful when generalised to quantum…
A quantum-classical decomposition of Gaussian quantum environments: a stochastic pseudomode model
Si Luo, Neill Lambert, Pengfei Liang +1
We show that the effect of a Gaussian Bosonic environment linearly coupled to a quantum system can be simulated by a stochastic Lindblad master equation characterized by a set of a…
Limits of single-photon storage in a single -type atom
Zhi-Lei Zhang, Li-Ping Yang
We theoretically investigate the limits of single-photon storage in a single -type atom, specifically the trade-off between storage efficiency and storage speed. We show that a…
Coherence-Assisted Superradiant Laser with Hz Linewidth and W Power
Guohui Dong, Yao Yao, Peng Zhang +1
The superradiant laser, based on the clock transition between the electric ground state S and the metastable state P of fermionic alkaline-earth(-like) atoms, has b…
Pre-training strategy for solving evolution equations based on physics-informed neural networks
Jiawei Guo, Yanzhong Yao, Han Wang +1
The physics informed neural network (PINN) is a promising method for solving time-evolution partial differential equations (PDEs). However, the standard PINN method may fail to sol…