A shallow hybrid classical-quantum spiking feedforward neural network for noise-robust image classification

被引:23
作者
Konar, Debanjan [1 ,2 ]
Das Sarma, Aditya [3 ]
Bhandary, Soham [3 ]
Bhattacharyya, Siddhartha [4 ]
Cangi, Attila [1 ,2 ]
Aggarwal, Vaneet [1 ,5 ,6 ]
机构
[1] CASUS Ctr Adv Syst Understanding, D-02826 Gorlitz, Germany
[2] Helmholtz Zent Dresden Rossendorf HZDR, D-01328 Dresden, Germany
[3] Jadavpur Univ, Dept Elect & Telecommun Engn, Kolkata 700092, India
[4] Rajnagar Mahavidyalaya, Birbhum 731130, West Bengal, India
[5] Purdue Univ, Sch Ind Engn IE, W Lafayette, IN 47907 USA
[6] Purdue Univ, Sch Elect & Comp Engn ECE, W Lafayette, IN 47907 USA
关键词
Quantum machine learning; Spiking neural networks; Variational quantum circuit; Hybrid classical-quantum neural networks;
D O I
10.1016/j.asoc.2023.110099
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Deep Convolutional Neural Network (CNN)-based image classification systems are often susceptible to noise interruption, i.e., minor image noise may significantly impact the outcome. On the contrary, classical Spiking Neural Network (SNN) is known for handling noisy data due to the stochastic and temporal behaviour of the spiking neuron signals. However, it is not always feasible to train the weights of the classical SNN due to stochastic and non-differentiable spiking signals. Recent applications of quantum machine learning to stochastic modelling have predicted that quantum computing would prove to be a significant advantage, hence catapulting the study of quantum and neuromorphic computing to a new level of development. Motivated by these observations, this paper introduces a shallow hybrid classical-quantum spiking feedforward neural network referred to as Spiking Quantum Neural Network (SQNN) for dealing with a robust image classification task in the presence of noise and adversarial attacks. The proposed SQNN offers the inherent capabilities of processing unforeseen noisy test images due to its spatial and temporal information. An efficient variational quantum circuit training with the help of a standard back-propagation algorithm obviates the classical Spiking Time-Dependent Plasticity (STDP) and Spike-Prop algorithms, which are often found inefficient in training the feedforward SNN. The proposed SQNN is extensively tested and benchmarked on the PennyLane Quantum Simulator. Experimental results show that the proposed SQNN model supersedes the feedforward SNN (SFNN), Random Quantum Neural Networks (RQNN), AlexNet and ResNet-18 on unseen test images with added noise from the FashionMNIST, MNIST, KMNIST, CIFAR10, and ImageNet datasets.(c) 2023 Elsevier B.V. All rights reserved.
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页数:16
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