Quantum Based Learning with Binary Neural Network

In this paper, a quantum based binary neural network learning algorithm is proposed for solving two class problems. The proposed method constructively forms the neural network architecture and weights are decided by quantum computing concept. The use of quantum computing optimizes the network structure and the performance in terms of number of neurons at hidden layer and classification accuracy. This approach is compared with MTiling-real networks algorithm and it is found that there is a significant improvement in terms of number of neurons at the hidden layer, number of iterations, training accuracy and generalization accuracy.