Open AccessEdge to quantum: hybrid quantum-spiking neural network image classifier
Author(s) -
Arun Ajayan,
Alex Pappachen James
Publication year - 2021
Publication title -
neuromorphic computing and engineering
Language(s) - English
Resource type - Journals
ISSN - 2634-4386
DOI - 10.1088/2634-4386/ac1cec
Subject(s) - mnist database , computer science , spiking neural network , artificial neural network , quantum , classifier (uml) , convolutional neural network , artificial intelligence , cellular neural network , algorithm , physics , quantum mechanics
The extreme parallelism property warrant convergence of neural networks with that of quantum computing. As the size of the network grows, the classical implementation of neural networks becomes computationally expensive and not feasible. In this paper, we propose a hybrid image classifier model using spiking neural networks (SNN) and quantum circuits that combines dynamic behaviour of SNN with the extreme parallelism offered by quantum computing. The proposed model outperforms models in comparison with spiking neural network in classical computing, and hybrid convolution neural network-quantum circuit models in terms of various performance parameters. The proposed hybrid SNN-QC model achieves an accuracy of 99.9% in comparison with CNN-QC model accuracy of 96.3%, and SNN model of accuracy 91.2% in MNIST classification task. The tests on KMNIST and CIFAR-1O also showed improvements.