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An Efficient Handwritten Character Recognition Using Quantum Multilayer Neural Network (QMLNN) Architecture: Quantum Multilayer Neural Network

An Efficient Handwritten Character Recognition Using Quantum Multilayer Neural Network (QMLNN) Architecture: Quantum Multilayer Neural Network

Debanjan Konar, Suman Kalyan Kar
Copyright: © 2018 |Pages: 15
ISBN13: 9781522552192|ISBN10: 1522552197|EISBN13: 9781522552208
DOI: 10.4018/978-1-5225-5219-2.ch008
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MLA

Konar, Debanjan, and Suman Kalyan Kar. "An Efficient Handwritten Character Recognition Using Quantum Multilayer Neural Network (QMLNN) Architecture: Quantum Multilayer Neural Network." Quantum-Inspired Intelligent Systems for Multimedia Data Analysis, edited by Siddhartha Bhattacharyya, IGI Global, 2018, pp. 262-276. https://doi.org/10.4018/978-1-5225-5219-2.ch008

APA

Konar, D. & Kar, S. K. (2018). An Efficient Handwritten Character Recognition Using Quantum Multilayer Neural Network (QMLNN) Architecture: Quantum Multilayer Neural Network. In S. Bhattacharyya (Ed.), Quantum-Inspired Intelligent Systems for Multimedia Data Analysis (pp. 262-276). IGI Global. https://doi.org/10.4018/978-1-5225-5219-2.ch008

Chicago

Konar, Debanjan, and Suman Kalyan Kar. "An Efficient Handwritten Character Recognition Using Quantum Multilayer Neural Network (QMLNN) Architecture: Quantum Multilayer Neural Network." In Quantum-Inspired Intelligent Systems for Multimedia Data Analysis, edited by Siddhartha Bhattacharyya, 262-276. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5219-2.ch008

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Abstract

This chapter proposes a quantum multi-layer neural network (QMLNN) architecture suitable for handwritten character recognition in real time, assisted by quantum backpropagation of errors calculated from the quantum-inspired fuzziness measure of network output states. It is composed of three second-order neighborhood-topology-based inter-connected layers of neurons represented by qubits known as input, hidden, and output layers. The QMLNN architecture is a feed forward network with standard quantum backpropagation algorithm for the adjustment of its weighted interconnection. QMLNN self-organizes the quantum fuzzy input image information by means of the quantum backpropagating errors at the intermediate and output layers of the architecture. The interconnection weights are described using rotation gates. After the network is stabilized, a quantum observation at the output layer destroys the superposition of quantum states in order to obtain true binary outputs.

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