An autonomous competitive learning algorithm using quantum hamming neural networks

Quantum neural network is a fledging research domain based on the merge of classical neural network and quantum computing. In this paper, we add the quantum effect to the classical hamming neural network algorithm in order to employ the advantages of quantum information to yield, finally, a novel quantum competitive learning algorithm. The proposed algorithm, called quantum hamming neural network (QHNN), is capable to recognize incomplete patterns, as well as, increase the probability of recognizing patterns on the account of undesired patterns. Moreover, these undesired patterns could be used as new patterns for training the algorithm in subsequent steps. The proposed algorithm is testified via a case study and a classification experiment, where promising results, reaches to 100%, are given and compared favorably with other reported quantum competitive algorithms.