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
Link To Document :
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