Dynamic Portfolio Selection to Counter Terrorism by using Quantum Neural Network Approach

Abstract

Not only Pakistan but the whole world is facing the problems of prevailing terrorist activities and attacks in many forms. Terrorism has diverse aspects and to eradicate this growing problem a hybrid model of quantum and classical neurons is suggested for the prediction of the risk involved and returns of investments in recommended areas to minimize terrorism. These areas are recommended on the basis of the findings of Crime analysts and professionals from other related domains after a deep analysis of the situation of the country and terrorist activities. The identification of the areas which causes terrorism is a core step towards counter the terrorism. Hopfield neural network is used to predict best possible portfolio from available resources. The recommended multilayer hybrid Quantum Neural Network holds hidden layer of quantum neurons while the visible layer is of classical neurons. With the help of QNN an appropriate portfolio can be selected whose risk factor will be minimum and the output generated from investments in identified areas will be maximum. 

Keywords:Quantum neural network, Portfolio selection, Resource allocation, Quantum back propagation, Quantum computation.