Binary Neuro-Fuzzy Classifiers Trained by Nonlinear Quantum Circuits

Authors:
Massimo Panella;Giuseppe Martinelli
Affiliations:
INFO-COM Dpt., University of Rome "La Sapienza", Via Eudossiana 18, 00184 Rome, Italy;INFO-COM Dpt., University of Rome "La Sapienza", Via Eudossiana 18, 00184 Rome, Italy
Venue:
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Year:
2007

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Abstract

The possibility of solving an optimization problem by an exhaustive search on all the possible solutions can advantageously replace traditional algorithms for learning neuro-fuzzy networks. For this purpose, the architecture of such networks should be tailored to the requirements of quantum processing. In particular, it is necessary to introduce superposition for pursuing parallelism and entanglement. In the present paper the specific case of neuro-fuzzy networks applied to binary classification is investigated. The peculiarity of the proposed method is the use of a nonlinear quantum algorithm for extracting the optimal neuro-fuzzy network. The computational complexity of the training process is considerably reduced with respect to the use of other classical approaches.