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

Debanjan Konar, Suman Kalyan Kar

Published Jan 1, 2021• 0 citations
Publisher

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.

DOI
10.4018/978-1-7998-8593-1.ch021

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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.
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.
Binary object extraction from a noisy perspective is a challenging proposition in the computer vision community. Various research initiatives based on different methodologies have been entrusted on this aspect over the decades. The multilayer self-organizing neural network (MLSONN) architecture implemented by a fuzzy measure guided backpropagation of errors is one of the best accomplishments in this direction. In this article, a quantum multilayer self-organizing neural network (QMLSONN) architecture is proposed to achieve the same objective. Quantum computation plays a remarkable role in the proposed architecture. The proposed architecture operates using single qubit rotation gates. Here different nodes and interconnection weights act as vital components for this intention. The proposed QMLSONN architecture comprises three processing layers specified as input, hidden and output layers. The nodes in the processing layers are represented by qubits and the interconnection weights are represented by quantum gates. A quantum measurement at the output layer destroys the quantum states of the processed information thereby inducing assimilation of linear indices of fuzziness as the network system errors used to adjust network interconnection weights through a proposed quantum back propagation algorithm. Results of application of the QMLSONN are established on a synthetic and a real life spanner image with various degrees of Gaussian noise and uniform noise. Comparative study with the classical MLSONN architecture reveals the time efficiency of the presented QMLSONN architecture. Moreover, the QMLSONN architecture also restores the shape of the extracted objects to a great extent.
Proper extraction of objects from a noisy perspective is an upheaval task in the computer vision research community. Several intelligent research paradigms have been focused on this aspect over the years. Notable among them is the multilayer self organizing neural network (MLSONN) architecture assisted by fuzzy measure guided back propagation of errors. In this article, we propose a quantum version of the MLSONN architecture which operates using single qubit rotation gates. The proposed QMLSONN architecture comprises three processing layers viz., input, hidden and output layers. The nodes of the processing layers are represented by qubits and the interconnection weights are represented by quantum gates. A quantum measurement at the output layer destroys the quantum states of the processed information thereby inducing incorporation of linear indices of fuzziness as the network system errors used to adjust network interconnection weights through a proposed quantum back propagation algorithm. Results of application of the QMLSONN are demonstrated on a synthetic and a real life spanner image with various degrees of Gaussian noise. A comparative study with the performance of the classical MLSONN architecture reveals the time efficiency of the proposed QMLSONN architecture.
A Quantum Bi-directional Self-Organizing Neural Network (QBDSONN) architecture suitable for binary image denoising in real time is proposed in this article. It is composed of three second order neighborhood topology based interconnected layers of neurons (represented by qubits) known as input, intermediate and output layers. Moreover, it does not use any quantum back-propagation algorithm for the adjustment of its interconnection weights. Instead, it resorts to a counter-propagation of quantum states of the intermediate layer and the output layer. In the proposed architecture, the inter-connection weights and activation values are represented by rotation gates. The quantum neurons of each network layer follow a cellular network pattern and are fully intra-connected to each other. QBDSONN self-organizes the quantized input image information by means of the counter-propagating fashion of the quantum network states of the intermediate and output layers of the architecture. A quantum measurement at the output layer collapses superposition of quantum states of the processed information thereby yielding the desired outputs once the network attains stability. Applications of QBDSONN are demonstrated on the denoising of a synthetic and real life spanner image with different degrees of uniform noise and Gaussian noise. Comparative results indicate that QBDSONN outperforms its classical counterpart in terms of time and also it retains the shapes of the denoised images with great precision.
This paper introduces a novel neural network architecture leveraging the principles of quantum entanglement to enhance parallel computation and learning efficiency. Traditional neural networks are fundamentally limited by the speed of light and the sequential processing of information. We propose a multi-layer parallel neural network where neurons are interconnected through entangled qubits. The synaptic weights are encoded within the entangled states, and the activation function utilizes the superposition property of entangled states for parallel processing. A learning algorithm based on the interference effects within entangled states is developed to adjust the synaptic weights. This approach theoretically enables exponential increases in parallel computation capability and faster learning rates compared to conventional neural networks. The core claim of this work is that quantum entanglement provides a mechanism for instantaneous information transfer between neurons, surpassing the limitations of classical physics and significantly boosting neural network performance.
Handwritten numeral recognition has generated significant interest in last several years owing to its sundry application potentials in the fields of image processing and computer vision. Recently, quantum neural networks (QNN) have been found to be efficient for information processing, such as image classification, recognition and optimization, owing to its distinct features. Therefore, in this study, a QNN based handwritten character recognition system is studied. Experiments are conducted with the data from the MNIST database. Back-propagation (BP) algorithm and particle swarm optimization (PSO) algorithm are used during the training process to improve the performance of the QNN. The resulting recognition rate of this proposed system is up to 99.16%. The results of the experiments clearly demonstrate the superiority of the proposed QNN in terms of its convergence speed and recognition rate as compared to other classical recognition methods, and simultaneously show the superiority of the QNN in solving character recognition problems.