There are various methods have been proposed for Pattern classifications with quantum neural networks. Mostly these methods are employing the Grover’s iteration on Bell’s MES in two-qubit system.  Further has been demonstrated that for any pattern classification in a two-qubit system the maximally entangled states of Singh-Rajput eigen basis provide the most suitable choice of search states and in no case any of Bell’s states is suitable for such pattern classifications. Here in this present work we are employing the quantum perceptron architecture which incorporates entanglement of weights and states both for producing the required pattern classification. The quantum perceptron learning rule is presented to train the network for the given training set and convergence and normalization of weights have been observed. The simulation results show that the proposed quantum perceptron neural network is capable to classify all the kinds of patterns whether the patterns are linearly separable or not.  

Keywords: Quantum neural networks, Machine learning, Pattern classification, Quantum information processing, Entanglement.