Pattern Classifications Using Quantum Neural Networks
Abstract
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.
Short Biography
Prof. Manu Pratap Singh received his Ph.D. from Kumaun University Nainital, Uthrakhand, India, in 2001. He completed his Master of Science in Computer Science from Allahabad University, Allahabad in 1995. He is currently working as Professor in Department of Computer Science, Institute of Engineering and Technology, Dr. B.R. Ambedkar University, Agra, UP, India since 2014. He is engaged in teaching and research since last 20 years. He has published more than 90 research papers in journals of international and national repute. His work has been recognized widely around the world in the form of citations of his research papers. He also has received the Young Scientist Award in computer science by international Academy of Physical sciences, Allahabad in year 2005. He has guided 18 students for their doctorate in computer science. He is also referee of various international and national journals. His research interests are focused on Neural networks, pattern recognition and machine intelligence, soft-computing, quantum computing etc. He also has a patent on machine learning.