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Computational Power of Quantum Artificial Neural Network

Sitakanta Nayak, Shaktikanta Nayak

Published Jan 1, 2011• 1 citation
OpenAlex

Abstract

Quantum Artificial Neural Network (QANN) is one of the new paradigm built upon the combination of classical neural computation and quantum computation. It has great values for theoretic study and potential to applications. In this paper an attempt is made to show how a single quantum neuron is able to perform the XOR function which is unrealizable with a single classical neuron and has the same computational power as the two-layer perceptron.

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Quantum Artificial Neural Network (QANN) is one of the new paradigm built upon the combination of classical neural computation and quantum computation. It has great values for theoretic study and potential to applications. In this paper an attempt is made to show how a single quantum neuron is able to perform the XOR function which is unrealizable with a single classical neuron and has the same computational power as the two-layer perceptron.
Quantum artificial neural network (QANN) is one of the new paradigms built upon the combination of classical neural and quantum computations. It has great values for theoretic study and potentials to applications. In this paper, an intrinsic similarity of quantum theory and artificial neural network (ANN) theory has been highlighted and analyzed. The dynamic features of QANN are also discussed in details. As an application using dynamic features, a novel model of quantum neuron (QN) is described hereby with a focus on an operation function. Numerical results are illustrated to show that this the proposed QN model can perform a XOR function unrealizable with a classical neuron (CN). The model allows obtain a nonlinear mapping performed by two layers of classical ANN (CANN)
The goal of the artificial neural network is to create powerful artificial problem solving systems. The field of quantum computationapplies ideas from quantum mechanics to the study of computation and has made interesting progress. Quantum Artificial Neural Network(QANN) is one of the new paradigms built upon the combination of classical neural computation and quantum computation. It has great valuesfor theoretic study and potential to applications. In this paper an attempt is made to show how a single quantum neuron is able to perform thewalsh-hadamard transformation which is an important unitary transformation often employed in quantum computation. Keywords: Quantum computation, walsh-hadamard transformation, Quantum neuron, Quantum artificial neural network
Quantum Neural Networks (QNN) is naturally the next step in the evolution of neuro-computing system. QNN may give us unprecedented possibilities in creating new system for information processing. The fundamentals of QNN are introduced, a model for quantum neuron is described. A quantum learning algorithm is proposed and its convergence property is discussed. The computational power of the quantum neuron is explored. Numerical and graphical results show that this single quantum neuron can perform XOR function unrealizable with a classical neuron and has the same computational power as the two-layer NN, it can be used to realize nonlinear mapping.
The goal of the artificial neural network is to create powerful artificial problem solving systems. The field of quantum computation applies ideas from quantum mechanics to the study of computation and has made interesting progress. Quantum Neural Network (QNN) is one of the new paradigms built upon the combination of classical neural computation and quantum computation. It is argued that the study of QNN may explain the brain functionality in a better way and create new systems for information processing including solving some classically intractable problems. In this paper we have given an introductory representation of quantum artificial neural network to show how it can be modelled on the basis of double-slit experiment. Also an attempt is made to show the quantum mechanical representation of a classical neuron to implement Hadamard transformation.