Quantum Neural Network in Recognition of Handwritten Numerals

This paper adopts a new kind of neural network — Quantum Neural Network (QNN) to recognize handwritten numerals. QNN combines the advantages of neural modelling and fuzzy theoretic principles. Novel experiments have been designed for the in-depth studies of applying the QNN to both synthesized confusing images and real data. Tests on synthesized data examine QNN's fuzzy feature space with an intention to illustrate its mechanism and characteristics, while studies on real data prove its great potential as a handwritten numeral classifier and the special role it plays in the multi-expert systems. Detailed comparisons and analyses of experimental results are given. An effective decision-fusion system is proposed and high reliability of 99.10% has been accomplished.