Title :
Implementing competitive learning in a quantum system
Author_Institution :
Fonix Corp., Salt Lake City. UT, USA
Abstract :
Ideas from quantum computation are applied to the field of neural networks to produce competitive learning in a quantum system. The resulting quantum competitive learner has a prototype storage capacity that is exponentially greater than that of its classical counterpart. Furthermore, empirical results from simulation of the quantum competitive learning system on real-world data sets demonstrate the quantum system´s potential for excellent performance
Keywords :
neural nets; pattern classification; quantum theory; unsupervised learning; competitive learning; neural networks; pattern classification; quantum system; storage capacity; Computational modeling; Computer networks; Hilbert space; Learning systems; Neural networks; Prototypes; Quantum computing; Quantum mechanics; Vectors; Wave functions;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.831539
