Automatic spin-chain learning to explore the quantum speed limit

Zhang, Xiao-Ming1,2; Cui, Zi-Wei3,4; Wang, Xin1,2; Yung, Man-Hong3,4,5

2018-05-30

10.1103/PhysRevA.97.052333

PHYSICAL REVIEW A   影响因子和分区

2469-9926

2469-9934

One of the ambitious goals of artificial intelligence is to build a machine that outperforms human intelligence, even if limited knowledge and data are provided. Reinforcement learning (RL) provides one such possibility to reach this goal. In this work, we consider a specific task from quantum physics, i.e., quantum state transfer in a one-dimensional spin chain. The mission for the machine is to find transfer schemes with the fastest speeds while maintaining high transfer fidelities. The first scenario we consider is when the Hamiltonian is time independent. We update the coupling strength by minimizing a loss function dependent on both the fidelity and the speed. Compared with a scheme proven to be at the quantum speed limit for the perfect state transfer, the scheme provided by RL is faster while maintaining the infidelity below 5 x 10(-4). In the second scenario where a time-dependent external field is introduced, we convert the state transfer process into a Markov decision process that can be understood by the machine. We solve it with the deep Q-learning algorithm. After training, the machine successfully finds transfer schemes with high fidelities and speeds, which are faster than previously known ones. These results show that reinforcement learning can be a powerful tool for quantum control problems.

SCI ; EI

英语

通讯

Science, Technology and Innovation Commission of Shenzhen Municipality[ZDSYS20170303165926217] ; Science, Technology and Innovation Commission of Shenzhen Municipality[JCYJ20170412152620376]

Optics ; Physics

Optics ; Physics, Atomic, Molecular & Chemical

WOS:000433418300003

AMER PHYSICAL SOC

20182405303288

Chains ; Couplings ; Deep learning ; Learning algorithms ; Markov processes ; Quantum theory ; Speed ; Spin dynamics

Mechanical Drives:602.1 ; Artificial Intelligence:723.4 ; Probability Theory:922.1 ; Atomic and Molecular Physics:931.3 ; Quantum Theory; Quantum Mechanics:931.4

PHYSICS

Web of Science

1.City Univ Hong Kong, Dept Phys, Tat Chee Ave, Kowloon, Hong Kong, Peoples R China
2.City Univ Hong Kong, Shenzhen Res Inst, Shenzhen 518057, Guangdong, Peoples R China
3.Southern Univ Sci & Technol, Inst Quantum Sci & Engn, Shenzhen 518055, Peoples R China
4.Southern Univ Sci & Technol, Dept Phys, Shenzhen 518055, Peoples R China
5.Shenzhen Key Lab Quantum Sci & Engn, Shenzhen 518055, Peoples R China

Zhang, Xiao-Ming,Cui, Zi-Wei,Wang, Xin,et al. Automatic spin-chain learning to explore the quantum speed limit[J]. PHYSICAL REVIEW A,2018,97(5).

Zhang, Xiao-Ming,Cui, Zi-Wei,Wang, Xin,&Yung, Man-Hong.(2018).Automatic spin-chain learning to explore the quantum speed limit.PHYSICAL REVIEW A,97(5).

Zhang, Xiao-Ming,et al."Automatic spin-chain learning to explore the quantum speed limit".PHYSICAL REVIEW A 97.5(2018).