Soft Computing https://doi.org/10.1007/s00500-017-2954-3 METHODOLOGIES AND APPLICATION Enhanced quantum-based neural network learning and its application to signature verification Om Prakash Patel 1 · Aruna Tiwari 1 · Rishabh Chaudhary 1 · Sai Vidyaranya Nuthalapati 1 · Neha Bharill 1 · Mukesh Prasad 2 · Farookh Khadeer Hussain 2 · Omar Khadeer Hussain 3 © Springer-Verlag GmbH Germany, part of Springer Nature 2017 Abstract In this paper, an enhanced quantum-based neural network learning algorithm (EQNN-S) which constructs a neural network architecture using the quantum computing concept is proposed for signature verification. The quantum computing concept is used to decide the connection weights and threshold of neurons. A boundary threshold parameter is introduced to optimally determine the neuron threshold. This parameter uses min, max function to decide threshold, which assists efficient learning. A manually prepared signature dataset is used to test the performance of the proposed algorithm. To uniquely identify the signature, several novel features are selected such as the number of loops present in the signature, the boundary calculation, the number of vertical and horizontal dense patches, and the angle measurement. A total of 45 features are extracted from each signature. The performance of the proposed algorithm is evaluated by rigorous training and testing with these signatures using partitions of 60–40 and 70–30%, and a tenfold cross-validation. To compare the results derived from the proposed quantum neural network, the same dataset is tested on support vector machine, multilayer perceptron, back propagation neural network, and Naive Bayes. The performance of the proposed algorithm is found better when compared with the above methods, and the results verify the effectiveness of the proposed algorithm. Keywords Quantum computing · Neural network · Signatures · Feature extraction · Classification Communicated by V. Loia. B Om Prakash Patel phd1301201003@iiti.ac.in B Mukesh Prasad mukesh.nctu@gmail.com Aruna Tiwari artiwari@iiti.ac.in Rishabh Chaudhary ee1200231@iiti.ac.in Sai Vidyaranya Nuthalapati ee1200221@iiti.ac.in Neha Bharill phd12120103@iiti.ac.in Farookh Khadeer Hussain Farookh.Hussain@uts.edu.au Omar Khadeer Hussain o.hussain@adfa.edu.au 1 Introduction Artificial neural network has emerged as one of the most promising areas of research in recent years. It is used for pattern recognition, classification, mining, clustering and many more (Castellani 2017; Sartakhti et al. 2017; Wang and Wang 2010; Gupta and Singh 2011). Researchers have pre- sented many learning algorithms, such as perceptron, back propagation and multilayer perceptron for solving both two- class and multi-class problems. The performance of a neural network system varies according to its architecture, hidden layers, the number of neurons in the hidden layer, connection weights, and threshold. It is also dependent on the features 1 Department of Computer Science and Engineering, Indian Institute of Technology Indore, Simrol Indore, Madhya Pradesh, India 2 Centre for Artificial Intelligence, School of Software, FEIT, University of Technology Sydney, Sydney, Australia 3 School of Business, University of New South Wales, Canberra, Australia 123