• DocumentCode
    460776
  • Title

    Control of Five-qubit System Based on Quantum Reinforcement Learning

  • Author

    Dong, Daoyi ; Chen, Chunlin ; Chen, Zonghai ; Zhang, Chenbin

  • Author_Institution
    Dept. of Autom., Univ. of Sci. & Technol. of China
  • Volume
    1
  • fYear
    2006
  • fDate
    Nov. 2006
  • Firstpage
    164
  • Lastpage
    167
  • Abstract
    Controlling the multi-qubit system is a key task for practical quantum information processing. In this paper, the control problem of five-qubit is studied. A novel quantum reinforcement learning algorithm based on quantum superposition principle is proposed for the quantum control problem. The simulated result shows that quantum reinforcement learning can effectively find the optimal control sequence through fast learning
  • Keywords
    control engineering computing; discrete systems; learning (artificial intelligence); optimal control; quantum theory; five-qubit system control; optimal control; quantum control; quantum information processing; quantum reinforcement learning; quantum superposition; Automatic control; Automation; Control systems; Information processing; Information theory; Learning; Nuclear magnetic resonance; Optimal control; Quantum computing; Quantum mechanics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Security, 2006 International Conference on
  • Conference_Location
    Guangzhou
  • Print_ISBN
    1-4244-0605-6
  • Electronic_ISBN
    1-4244-0605-6
  • Type

    conf

  • DOI
    10.1109/ICCIAS.2006.294113
  • Filename
    4072066