418 citations
- Chinese Academy of SciencesCN76 papers
- National Center for Mathematics and Interdisciplinary SciencesCN7 papers
- Beijing Normal UniversityCN6 papers
- Capital Normal UniversityCN6 papers
- University of Chinese Academy of SciencesCN6 papers
- Kavli Institute for Theoretical SciencesCN5 papers
- Peking UniversityCN4 papers
- The University of SydneyAU4 papers
- Zhejiang UniversityCN4 papers
- Central University of Finance and EconomicsCN3 papers
- Fudan UniversityCN3 papers
- Institut de Mathématiques de BordeauxFR3 papers
6 papers · 1 filter
Dynamical Decoupling in Common Environment
Yu Pan, Hong-Ting Song, Zai-Rong Xi
Dynamical decoupling (DD) sequences were invented to eliminate the direct coupling between qubit and its environment. We further investigate the possibility of decoupling the indir…
Hierarchy of measurement-induced Fisher information for composite states
Xiao-Ming Lu, Shunlong Luo, C. H. Oh
Quantum Fisher information, as an intrinsic quantity for quantum states, is a central concept in quantum detection and estimation. When quantum measurements are performed on quantu…
Model-Checking Linear-Time Properties of Quantum Systems
Mingsheng Ying, Yangjia Li, Nengkun Yu +1
We define a formal framework for reasoning about linear-time properties of quantum systems in which quantum automata are employed in the modeling of systems and certain closed subs…
Non-Markovian entanglement dynamics between two coupled qubits in the same environment
Wei Cui, Zairong Xi, Yu Pan
We analyze the dynamics of the entanglement in two independent non-Markovian channels. In particular, we focus on the entanglement dynamics as a function of the initial states and…
Optimal Decoherence Control in non-Markovian Open, Dissipative Quantum Systems
Wei Cui, Zairong Xi, Yu Pan
We investigate the optimal control problem for non-Markovian open, dissipative quantum system. Optimal control using Pontryagin maximum principle is specifically derived. The influ…
Quantum reinforcement learning
Daoyi Dong, Chunlin Chen, Hanxiong Li +1
The key approaches for machine learning, especially learning in unknown probabilistic environments are new representations and computation mechanisms. In this paper, a novel quantu…